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['Jinsong Tan']

0704.2092v2

We consider inapproximability of the correlation clustering problem defined as follows: Given a graph $G = (V,E)$ where each edge is labeled either "+" (similar) or "-" (dissimilar), correlation clustering seeks to partition the vertices into clusters so that the number of pairs correctly (resp. incorrectly) classified with respect to the labels is maximized (resp. minimized). The two complementary problems are called MaxAgree and MinDisagree, respectively, and have been studied on complete graphs, where every edge is labeled, and general graphs, where some edge might not have been labeled. Natural edge-weighted versions of both problems have been studied as well. Let S-MaxAgree denote the weighted problem where all weights are taken from set S, we show that S-MaxAgree with weights bounded by $O(|V|^{1/2-\delta})$ essentially belongs to the same hardness class in the following sense: if there is a polynomial time algorithm that approximates S-MaxAgree within a factor of $\lambda = O(\log{|V|})$ with high probability, then for any choice of S', S'-MaxAgree can be approximated in polynomial time within a factor of $(\lambda + \epsilon)$, where $\epsilon > 0$ can be arbitrarily small, with high probability. A similar statement also holds for $S-MinDisagree. This result implies it is hard (assuming $NP \neq RP$) to approximate unweighted MaxAgree within a factor of $80/79-\epsilon$, improving upon a previous known factor of $116/115-\epsilon$ by Charikar et. al. \cite{Chari05}.

A Note on the Inapproximability of Correlation Clustering

http://arxiv.org/pdf/0704.2092v2

Title Note Inapproximability Correlation Clustering Summary consider inapproximability correlation clustering problem defined follows Given graph G edge labeled either similar dissimilar correlation clustering seek partition vertex cluster number pair correctly resp incorrectly classified respect label maximized resp minimized two complementary problem called MaxAgree MinDisagree respectively studied complete graph every edge labeled general graph edge might labeled Natural edgeweighted version problem studied well Let SMaxAgree denote weighted problem weight taken set show SMaxAgree weight bounded OV12delta essentially belongs hardness class following sense polynomial time algorithm approximates SMaxAgree within factor lambda OlogV high probability choice SMaxAgree approximated polynomial time within factor lambda epsilon epsilon 0 arbitrarily small high probability similar statement also hold SMinDisagree result implies hard assuming NP neq RP approximate unweighted MaxAgree within factor 8079epsilon improving upon previous known factor 116115epsilon Charikar et al citeChari05 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ulrike von Luxburg']

0711.0189v1

In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.

A Tutorial on Spectral Clustering

http://arxiv.org/pdf/0711.0189v1

Title Tutorial Spectral Clustering Summary recent year spectral clustering become one popular modern clustering algorithm simple implement solved efficiently standard linear algebra software often outperforms traditional clustering algorithm kmeans algorithm first glance spectral clustering appears slightly mysterious obvious see work really goal tutorial give intuition question describe different graph Laplacians basic property present common spectral clustering algorithm derive algorithm scratch several different approach Advantages disadvantage different spectral clustering algorithm discussed Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Xinhua Zhang', 'Ankan Saha', 'S. V. N. Vishwanathan']

1003.1354v1

Structured output prediction is an important machine learning problem both in theory and practice, and the max-margin Markov network (\mcn) is an effective approach. All state-of-the-art algorithms for optimizing \mcn\ objectives take at least $O(1/\epsilon)$ number of iterations to find an $\epsilon$ accurate solution. Recent results in structured optimization suggest that faster rates are possible by exploiting the structure of the objective function. Towards this end \citet{Nesterov05} proposed an excessive gap reduction technique based on Euclidean projections which converges in $O(1/\sqrt{\epsilon})$ iterations on strongly convex functions. Unfortunately when applied to \mcn s, this approach does not admit graphical model factorization which, as in many existing algorithms, is crucial for keeping the cost per iteration tractable. In this paper, we present a new excessive gap reduction technique based on Bregman projections which admits graphical model factorization naturally, and converges in $O(1/\sqrt{\epsilon})$ iterations. Compared with existing algorithms, the convergence rate of our method has better dependence on $\epsilon$ and other parameters of the problem, and can be easily kernelized.

Faster Rates for training Max-Margin Markov Networks

http://arxiv.org/pdf/1003.1354v1

Title Faster Rates training MaxMargin Markov Networks Summary Structured output prediction important machine learning problem theory practice maxmargin Markov network mcn effective approach stateoftheart algorithm optimizing mcn objective take least O1epsilon number iteration find epsilon accurate solution Recent result structured optimization suggest faster rate possible exploiting structure objective function Towards end citetNesterov05 proposed excessive gap reduction technique based Euclidean projection converges O1sqrtepsilon iteration strongly convex function Unfortunately applied mcn approach admit graphical model factorization many existing algorithm crucial keeping cost per iteration tractable paper present new excessive gap reduction technique based Bregman projection admits graphical model factorization naturally converges O1sqrtepsilon iteration Compared existing algorithm convergence rate method better dependence epsilon parameter problem easily kernelized Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mofreh A. Hogo']

1003.1499v1

This paper introduces an evaluation methodologies for the e-learners' behaviour that will be a feedback to the decision makers in e-learning system. Learner's profile plays a crucial role in the evaluation process to improve the e-learning process performance. The work focuses on the clustering of the e-learners based on their behaviour into specific categories that represent the learner's profiles. The learners' classes named as regular, workers, casual, bad, and absent. The work may answer the question of how to return bad students to be regular ones. The work presented the use of different fuzzy clustering techniques as fuzzy c-means and kernelized fuzzy c-means to find the learners' categories and predict their profiles. The paper presents the main phases as data description, preparation, features selection, and the experiments design using different fuzzy clustering models. Analysis of the obtained results and comparison with the real world behavior of those learners proved that there is a match with percentage of 78%. Fuzzy clustering reflects the learners' behavior more than crisp clustering. Comparison between FCM and KFCM proved that the KFCM is much better than FCM in predicting the learners' behaviour.

Evaluation of E-Learners Behaviour using Different Fuzzy Clustering Models: A Comparative Study

http://arxiv.org/pdf/1003.1499v1

Title Evaluation ELearners Behaviour using Different Fuzzy Clustering Models Comparative Study Summary paper introduces evaluation methodology elearners behaviour feedback decision maker elearning system Learners profile play crucial role evaluation process improve elearning process performance work focus clustering elearners based behaviour specific category represent learner profile learner class named regular worker casual bad absent work may answer question return bad student regular one work presented use different fuzzy clustering technique fuzzy cmeans kernelized fuzzy cmeans find learner category predict profile paper present main phase data description preparation feature selection experiment design using different fuzzy clustering model Analysis obtained result comparison real world behavior learner proved match percentage 78 Fuzzy clustering reflects learner behavior crisp clustering Comparison FCM KFCM proved KFCM much better FCM predicting learner behaviour Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Vidhya. K. A', 'G. Aghila']

1003.1795v1

Text Document classification aims in associating one or more predefined categories based on the likelihood suggested by the training set of labeled documents. Many machine learning algorithms play a vital role in training the system with predefined categories among which Na\"ive Bayes has some intriguing facts that it is simple, easy to implement and draws better accuracy in large datasets in spite of the na\"ive dependence. The importance of Na\"ive Bayes Machine learning approach has felt hence the study has been taken up for text document classification and the statistical event models available. This survey the various feature selection methods has been discussed and compared along with the metrics related to text document classification.

A Survey of Naïve Bayes Machine Learning approach in Text Document Classification

http://arxiv.org/pdf/1003.1795v1

Title Survey Naïve Bayes Machine Learning approach Text Document Classification Summary Text Document classification aim associating one predefined category based likelihood suggested training set labeled document Many machine learning algorithm play vital role training system predefined category among Naive Bayes intriguing fact simple easy implement draw better accuracy large datasets spite naive dependence importance Naive Bayes Machine learning approach felt hence study taken text document classification statistical event model available survey various feature selection method discussed compared along metric related text document classification Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Blaine Nelson', 'Benjamin I. P. Rubinstein', 'Ling Huang', 'Anthony D. Joseph', 'Shing-hon Lau', 'Steven J. Lee', 'Satish Rao', 'Anthony Tran', 'J. D. Tygar']

1003.2751v1

Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade detection at near-minimal cost. We generalize results of Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms that construct undetected instances of near-minimal cost using only polynomially many queries in the dimension of the space and without reverse engineering the decision boundary.

Near-Optimal Evasion of Convex-Inducing Classifiers

http://arxiv.org/pdf/1003.2751v1

Title NearOptimal Evasion ConvexInducing Classifiers Summary Classifiers often used detect miscreant activity study adversary efficiently query classifier elicit information allows adversary evade detection nearminimal cost generalize result Lowd Meek 2005 convexinducing classifier present algorithm construct undetected instance nearminimal cost using polynomially many query dimension space without reverse engineering decision boundary Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Peter Duersch', 'Joerg Oechssler', 'Burkhard C. Schipper']

1003.4274v1

We show that for many classes of symmetric two-player games, the simple decision rule "imitate-the-best" can hardly be beaten by any other decision rule. We provide necessary and sufficient conditions for imitation to be unbeatable and show that it can only be beaten by much in games that are of the rock-scissors-paper variety. Thus, in many interesting examples, like 2x2 games, Cournot duopoly, price competition, rent seeking, public goods games, common pool resource games, minimum effort coordination games, arms race, search, bargaining, etc., imitation cannot be beaten by much even by a very clever opponent.

Unbeatable Imitation

http://arxiv.org/pdf/1003.4274v1

Title Unbeatable Imitation Summary show many class symmetric twoplayer game simple decision rule imitatethebest hardly beaten decision rule provide necessary sufficient condition imitation unbeatable show beaten much game rockscissorspaper variety Thus many interesting example like 2x2 game Cournot duopoly price competition rent seeking public good game common pool resource game minimum effort coordination game arm race search bargaining etc imitation cannot beaten much even clever opponent Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pawan Kumar', 'Astik Biswas', 'A . N. Mishra', 'Mahesh Chandra']

1003.5623v1

This paper introduces and motivates the use of hybrid robust feature extraction technique for spoken language identification (LID) system. The speech recognizers use a parametric form of a signal to get the most important distinguishable features of speech signal for recognition task. In this paper Mel-frequency cepstral coefficients (MFCC), Perceptual linear prediction coefficients (PLP) along with two hybrid features are used for language Identification. Two hybrid features, Bark Frequency Cepstral Coefficients (BFCC) and Revised Perceptual Linear Prediction Coefficients (RPLP) were obtained from combination of MFCC and PLP. Two different classifiers, Vector Quantization (VQ) with Dynamic Time Warping (DTW) and Gaussian Mixture Model (GMM) were used for classification. The experiment shows better identification rate using hybrid feature extraction techniques compared to conventional feature extraction methods.BFCC has shown better performance than MFCC with both classifiers. RPLP along with GMM has shown best identification performance among all feature extraction techniques.

Spoken Language Identification Using Hybrid Feature Extraction Methods

http://arxiv.org/pdf/1003.5623v1

Title Spoken Language Identification Using Hybrid Feature Extraction Methods Summary paper introduces motivates use hybrid robust feature extraction technique spoken language identification LID system speech recognizers use parametric form signal get important distinguishable feature speech signal recognition task paper Melfrequency cepstral coefficient MFCC Perceptual linear prediction coefficient PLP along two hybrid feature used language Identification Two hybrid feature Bark Frequency Cepstral Coefficients BFCC Revised Perceptual Linear Prediction Coefficients RPLP obtained combination MFCC PLP Two different classifier Vector Quantization VQ Dynamic Time Warping DTW Gaussian Mixture Model GMM used classification experiment show better identification rate using hybrid feature extraction technique compared conventional feature extraction methodsBFCC shown better performance MFCC classifier RPLP along GMM shown best identification performance among feature extraction technique Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mahmoud I. Abdalla', 'Hanaa S. Ali']

1003.5627v1

To improve the performance of speaker identification systems, an effective and robust method is proposed to extract speech features, capable of operating in noisy environment. Based on the time-frequency multi-resolution property of wavelet transform, the input speech signal is decomposed into various frequency channels. For capturing the characteristic of the signal, the Mel-Frequency Cepstral Coefficients (MFCCs) of the wavelet channels are calculated. Hidden Markov Models (HMMs) were used for the recognition stage as they give better recognition for the speaker's features than Dynamic Time Warping (DTW). Comparison of the proposed approach with the MFCCs conventional feature extraction method shows that the proposed method not only effectively reduces the influence of noise, but also improves recognition. A recognition rate of 99.3% was obtained using the proposed feature extraction technique compared to 98.7% using the MFCCs. When the test patterns were corrupted by additive white Gaussian noise with 20 dB S/N ratio, the recognition rate was 97.3% using the proposed method compared to 93.3% using the MFCCs.

Wavelet-Based Mel-Frequency Cepstral Coefficients for Speaker Identification using Hidden Markov Models

http://arxiv.org/pdf/1003.5627v1

Title WaveletBased MelFrequency Cepstral Coefficients Speaker Identification using Hidden Markov Models Summary improve performance speaker identification system effective robust method proposed extract speech feature capable operating noisy environment Based timefrequency multiresolution property wavelet transform input speech signal decomposed various frequency channel capturing characteristic signal MelFrequency Cepstral Coefficients MFCCs wavelet channel calculated Hidden Markov Models HMMs used recognition stage give better recognition speaker feature Dynamic Time Warping DTW Comparison proposed approach MFCCs conventional feature extraction method show proposed method effectively reduces influence noise also improves recognition recognition rate 993 obtained using proposed feature extraction technique compared 987 using MFCCs test pattern corrupted additive white Gaussian noise 20 dB SN ratio recognition rate 973 using proposed method compared 933 using MFCCs Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mithun Das Gupta']

1007.0380v1

Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. We interpret the factorization in a new way and use it to generate missing attributes from test data. We provide a joint optimization scheme for the missing attributes as well as the NMF factors. We prove the monotonic convergence of our algorithms. We present classification results for cases with missing attributes.

Additive Non-negative Matrix Factorization for Missing Data

http://arxiv.org/pdf/1007.0380v1

Title Additive Nonnegative Matrix Factorization Missing Data Summary Nonnegative matrix factorization NMF previously shown useful decomposition multivariate data interpret factorization new way use generate missing attribute test data provide joint optimization scheme missing attribute well NMF factor prove monotonic convergence algorithm present classification result case missing attribute Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Cem Tekin', 'Mingyan Liu']

1007.2238v2

We consider the classical multi-armed bandit problem with Markovian rewards. When played an arm changes its state in a Markovian fashion while it remains frozen when not played. The player receives a state-dependent reward each time it plays an arm. The number of states and the state transition probabilities of an arm are unknown to the player. The player's objective is to maximize its long-term total reward by learning the best arm over time. We show that under certain conditions on the state transition probabilities of the arms, a sample mean based index policy achieves logarithmic regret uniformly over the total number of trials. The result shows that sample mean based index policies can be applied to learning problems under the rested Markovian bandit model without loss of optimality in the order. Moreover, comparision between Anantharam's index policy and UCB shows that by choosing a small exploration parameter UCB can have a smaller regret than Anantharam's index policy.

Online Algorithms for the Multi-Armed Bandit Problem with Markovian Rewards

http://arxiv.org/pdf/1007.2238v2

Title Online Algorithms MultiArmed Bandit Problem Markovian Rewards Summary consider classical multiarmed bandit problem Markovian reward played arm change state Markovian fashion remains frozen played player receives statedependent reward time play arm number state state transition probability arm unknown player player objective maximize longterm total reward learning best arm time show certain condition state transition probability arm sample mean based index policy achieves logarithmic regret uniformly total number trial result show sample mean based index policy applied learning problem rested Markovian bandit model without loss optimality order Moreover comparision Anantharams index policy UCB show choosing small exploration parameter UCB smaller regret Anantharams index policy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Md. Saiful Islam', 'Md. Iftekharul Amin']

1008.4669v1

In this paper, we have proposed an architecture of active learning SVMs with relevance feedback (RF)for classifying e-mail. This architecture combines both active learning strategies where instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels of some number of them and relevance feedback where if any mail misclassified then the next set of support vectors will be different from the present set otherwise the next set will not change. Our proposed architecture will ensure that a legitimate e-mail will not be dropped in the event of overflowing mailbox. The proposed architecture also exhibits dynamic updating characteristics making life as difficult for the spammer as possible.

An Architecture of Active Learning SVMs with Relevance Feedback for Classifying E-mail

http://arxiv.org/pdf/1008.4669v1

Title Architecture Active Learning SVMs Relevance Feedback Classifying Email Summary paper proposed architecture active learning SVMs relevance feedback RFfor classifying email architecture combine active learning strategy instead using randomly selected training set learner access pool unlabeled instance request label number relevance feedback mail misclassified next set support vector different present set otherwise next set change proposed architecture ensure legitimate email dropped event overflowing mailbox proposed architecture also exhibit dynamic updating characteristic making life difficult spammer possible Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Vladimir Pestov']

1008.5105v5

Degrading performance of indexing schemes for exact similarity search in high dimensions has long since been linked to histograms of distributions of distances and other 1-Lipschitz functions getting concentrated. We discuss this observation in the framework of the phenomenon of concentration of measure on the structures of high dimension and the Vapnik-Chervonenkis theory of statistical learning.

Indexability, concentration, and VC theory

http://arxiv.org/pdf/1008.5105v5

Title Indexability concentration VC theory Summary Degrading performance indexing scheme exact similarity search high dimension long since linked histogram distribution distance 1Lipschitz function getting concentrated discus observation framework phenomenon concentration measure structure high dimension VapnikChervonenkis theory statistical learning Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Qihang Lin', 'Xi Chen', 'Javier Pena']

1008.5204v2

We consider the unconstrained optimization problem whose objective function is composed of a smooth and a non-smooth conponents where the smooth component is the expectation a random function. This type of problem arises in some interesting applications in machine learning. We propose a stochastic gradient descent algorithm for this class of optimization problem. When the non-smooth component has a particular structure, we propose another stochastic gradient descent algorithm by incorporating a smoothing method into our first algorithm. The proofs of the convergence rates of these two algorithms are given and we show the numerical performance of our algorithm by applying them to regularized linear regression problems with different sets of synthetic data.

A Smoothing Stochastic Gradient Method for Composite Optimization

http://arxiv.org/pdf/1008.5204v2

Title Smoothing Stochastic Gradient Method Composite Optimization Summary consider unconstrained optimization problem whose objective function composed smooth nonsmooth conponents smooth component expectation random function type problem arises interesting application machine learning propose stochastic gradient descent algorithm class optimization problem nonsmooth component particular structure propose another stochastic gradient descent algorithm incorporating smoothing method first algorithm proof convergence rate two algorithm given show numerical performance algorithm applying regularized linear regression problem different set synthetic data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Maria Florina Balcan', 'Yingyu Liang']

1112.0826v5

Motivated by the fact that distances between data points in many real-world clustering instances are often based on heuristic measures, Bilu and Linial~\cite{BL} proposed analyzing objective based clustering problems under the assumption that the optimum clustering to the objective is preserved under small multiplicative perturbations to distances between points. The hope is that by exploiting the structure in such instances, one can overcome worst case hardness results. In this paper, we provide several results within this framework. For center-based objectives, we present an algorithm that can optimally cluster instances resilient to perturbations of factor $(1 + \sqrt{2})$, solving an open problem of Awasthi et al.~\cite{ABS10}. For $k$-median, a center-based objective of special interest, we additionally give algorithms for a more relaxed assumption in which we allow the optimal solution to change in a small $\epsilon$ fraction of the points after perturbation. We give the first bounds known for $k$-median under this more realistic and more general assumption. We also provide positive results for min-sum clustering which is typically a harder objective than center-based objectives from approximability standpoint. Our algorithms are based on new linkage criteria that may be of independent interest. Additionally, we give sublinear-time algorithms, showing algorithms that can return an implicit clustering from only access to a small random sample.

Clustering under Perturbation Resilience

http://arxiv.org/pdf/1112.0826v5

Title Clustering Perturbation Resilience Summary Motivated fact distance data point many realworld clustering instance often based heuristic measure Bilu LinialciteBL proposed analyzing objective based clustering problem assumption optimum clustering objective preserved small multiplicative perturbation distance point hope exploiting structure instance one overcome worst case hardness result paper provide several result within framework centerbased objective present algorithm optimally cluster instance resilient perturbation factor 1 sqrt2 solving open problem Awasthi et alciteABS10 kmedian centerbased objective special interest additionally give algorithm relaxed assumption allow optimal solution change small epsilon fraction point perturbation give first bound known kmedian realistic general assumption also provide positive result minsum clustering typically harder objective centerbased objective approximability standpoint algorithm based new linkage criterion may independent interest Additionally give sublineartime algorithm showing algorithm return implicit clustering access small random sample Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Joseph Modayil', 'Adam White', 'Richard S. Sutton']

1112.1133v3

The term "nexting" has been used by psychologists to refer to the propensity of people and many other animals to continually predict what will happen next in an immediate, local, and personal sense. The ability to "next" constitutes a basic kind of awareness and knowledge of one's environment. In this paper we present results with a robot that learns to next in real time, predicting thousands of features of the world's state, including all sensory inputs, at timescales from 0.1 to 8 seconds. This was achieved by treating each state feature as a reward-like target and applying temporal-difference methods to learn a corresponding value function with a discount rate corresponding to the timescale. We show that two thousand predictions, each dependent on six thousand state features, can be learned and updated online at better than 10Hz on a laptop computer, using the standard TD(lambda) algorithm with linear function approximation. We show that this approach is efficient enough to be practical, with most of the learning complete within 30 minutes. We also show that a single tile-coded feature representation suffices to accurately predict many different signals at a significant range of timescales. Finally, we show that the accuracy of our learned predictions compares favorably with the optimal off-line solution.

Multi-timescale Nexting in a Reinforcement Learning Robot

http://arxiv.org/pdf/1112.1133v3

Title Multitimescale Nexting Reinforcement Learning Robot Summary term nexting used psychologist refer propensity people many animal continually predict happen next immediate local personal sense ability next constitutes basic kind awareness knowledge one environment paper present result robot learns next real time predicting thousand feature world state including sensory input timescales 01 8 second achieved treating state feature rewardlike target applying temporaldifference method learn corresponding value function discount rate corresponding timescale show two thousand prediction dependent six thousand state feature learned updated online better 10Hz laptop computer using standard TDlambda algorithm linear function approximation show approach efficient enough practical learning complete within 30 minute also show single tilecoded feature representation suffices accurately predict many different signal significant range timescales Finally show accuracy learned prediction compare favorably optimal offline solution Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dominique Barth', 'Boubkeur Boudaoud', 'Thierry Mautor']

1112.1615v1

The new model that we present in this paper is introduced in the context of guaranteed QoS and resources management in the inter-domain routing framework. This model, called the stock model, is based on a reverse cascade approach and is applied in a distributed context. So transit providers have to learn the right capacities to buy and to stock and, therefore learning theory is applied through an iterative process. We show that transit providers manage to learn how to strategically choose their capacities on each route in order to maximize their benefits, despite the very incomplete information. Finally, we provide and analyse some simulation results given by the application of the model in a simple case where the model quickly converges to a stable state.

SLA Establishment with Guaranteed QoS in the Interdomain Network: A Stock Model

http://arxiv.org/pdf/1112.1615v1

Title SLA Establishment Guaranteed QoS Interdomain Network Stock Model Summary new model present paper introduced context guaranteed QoS resource management interdomain routing framework model called stock model based reverse cascade approach applied distributed context transit provider learn right capacity buy stock therefore learning theory applied iterative process show transit provider manage learn strategically choose capacity route order maximize benefit despite incomplete information Finally provide analyse simulation result given application model simple case model quickly converges stable state Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Marcos Aurélio Domingues', 'Solange Oliveira Rezende']

1112.1734v1

The Data Mining process enables the end users to analyze, understand and use the extracted knowledge in an intelligent system or to support in the decision-making processes. However, many algorithms used in the process encounter large quantities of patterns, complicating the analysis of the patterns. This fact occurs with association rules, a Data Mining technique that tries to identify intrinsic patterns in large data sets. A method that can help the analysis of the association rules is the use of taxonomies in the step of post-processing knowledge. In this paper, the GART algorithm is proposed, which uses taxonomies to generalize association rules, and the RulEE-GAR computational module, that enables the analysis of the generalized rules.

Using Taxonomies to Facilitate the Analysis of the Association Rules

http://arxiv.org/pdf/1112.1734v1

Title Using Taxonomies Facilitate Analysis Association Rules Summary Data Mining process enables end user analyze understand use extracted knowledge intelligent system support decisionmaking process However many algorithm used process encounter large quantity pattern complicating analysis pattern fact occurs association rule Data Mining technique try identify intrinsic pattern large data set method help analysis association rule use taxonomy step postprocessing knowledge paper GART algorithm proposed us taxonomy generalize association rule RulEEGAR computational module enables analysis generalized rule Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Chih-Yu Wang', 'Yan Chen', 'K. J. Ray Liu']

1112.2187v2

In Part I of this two-part paper [1], we proposed a new game, called Chinese restaurant game, to analyze the social learning problem with negative network externality. The best responses of agents in the Chinese restaurant game with imperfect signals are constructed through a recursive method, and the influence of both learning and network externality on the utilities of agents is studied. In Part II of this two-part paper, we illustrate three applications of Chinese restaurant game in wireless networking, cloud computing, and online social networking. For each application, we formulate the corresponding problem as a Chinese restaurant game and analyze how agents learn and make strategic decisions in the problem. The proposed method is compared with four common-sense methods in terms of agents' utilities and the overall system performance through simulations. We find that the proposed Chinese restaurant game theoretic approach indeed helps agents make better decisions and improves the overall system performance. Furthermore, agents with different decision orders have different advantages in terms of their utilities, which also verifies the conclusions drawn in Part I of this two-part paper.

Chinese Restaurant Game - Part II: Applications to Wireless Networking, Cloud Computing, and Online Social Networking

http://arxiv.org/pdf/1112.2187v2

Title Chinese Restaurant Game Part II Applications Wireless Networking Cloud Computing Online Social Networking Summary Part twopart paper 1 proposed new game called Chinese restaurant game analyze social learning problem negative network externality best response agent Chinese restaurant game imperfect signal constructed recursive method influence learning network externality utility agent studied Part II twopart paper illustrate three application Chinese restaurant game wireless networking cloud computing online social networking application formulate corresponding problem Chinese restaurant game analyze agent learn make strategic decision problem proposed method compared four commonsense method term agent utility overall system performance simulation find proposed Chinese restaurant game theoretic approach indeed help agent make better decision improves overall system performance Furthermore agent different decision order different advantage term utility also verifies conclusion drawn Part twopart paper Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Chih-Yu Wang', 'Yan Chen', 'K. J. Ray Liu']

1112.2188v3

In a social network, agents are intelligent and have the capability to make decisions to maximize their utilities. They can either make wise decisions by taking advantages of other agents' experiences through learning, or make decisions earlier to avoid competitions from huge crowds. Both these two effects, social learning and negative network externality, play important roles in the decision process of an agent. While there are existing works on either social learning or negative network externality, a general study on considering both these two contradictory effects is still limited. We find that the Chinese restaurant process, a popular random process, provides a well-defined structure to model the decision process of an agent under these two effects. By introducing the strategic behavior into the non-strategic Chinese restaurant process, in Part I of this two-part paper, we propose a new game, called Chinese Restaurant Game, to formulate the social learning problem with negative network externality. Through analyzing the proposed Chinese restaurant game, we derive the optimal strategy of each agent and provide a recursive method to achieve the optimal strategy. How social learning and negative network externality influence each other under various settings is also studied through simulations.

Chinese Restaurant Game - Part I: Theory of Learning with Negative Network Externality

http://arxiv.org/pdf/1112.2188v3

Title Chinese Restaurant Game Part Theory Learning Negative Network Externality Summary social network agent intelligent capability make decision maximize utility either make wise decision taking advantage agent experience learning make decision earlier avoid competition huge crowd two effect social learning negative network externality play important role decision process agent existing work either social learning negative network externality general study considering two contradictory effect still limited find Chinese restaurant process popular random process provides welldefined structure model decision process agent two effect introducing strategic behavior nonstrategic Chinese restaurant process Part twopart paper propose new game called Chinese Restaurant Game formulate social learning problem negative network externality analyzing proposed Chinese restaurant game derive optimal strategy agent provide recursive method achieve optimal strategy social learning negative network externality influence various setting also studied simulation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['B. Mishra', 'G. Meyer', 'F. Bach', 'R. Sepulchre']

1112.2318v2

The paper addresses the problem of low-rank trace norm minimization. We propose an algorithm that alternates between fixed-rank optimization and rank-one updates. The fixed-rank optimization is characterized by an efficient factorization that makes the trace norm differentiable in the search space and the computation of duality gap numerically tractable. The search space is nonlinear but is equipped with a particular Riemannian structure that leads to efficient computations. We present a second-order trust-region algorithm with a guaranteed quadratic rate of convergence. Overall, the proposed optimization scheme converges super-linearly to the global solution while maintaining complexity that is linear in the number of rows and columns of the matrix. To compute a set of solutions efficiently for a grid of regularization parameters we propose a predictor-corrector approach that outperforms the naive warm-restart approach on the fixed-rank quotient manifold. The performance of the proposed algorithm is illustrated on problems of low-rank matrix completion and multivariate linear regression.

Low-rank optimization with trace norm penalty

http://arxiv.org/pdf/1112.2318v2

Title Lowrank optimization trace norm penalty Summary paper address problem lowrank trace norm minimization propose algorithm alternate fixedrank optimization rankone update fixedrank optimization characterized efficient factorization make trace norm differentiable search space computation duality gap numerically tractable search space nonlinear equipped particular Riemannian structure lead efficient computation present secondorder trustregion algorithm guaranteed quadratic rate convergence Overall proposed optimization scheme converges superlinearly global solution maintaining complexity linear number row column matrix compute set solution efficiently grid regularization parameter propose predictorcorrector approach outperforms naive warmrestart approach fixedrank quotient manifold performance proposed algorithm illustrated problem lowrank matrix completion multivariate linear regression Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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-0.03044503554701805, 0.029254388064146042, -0.016134291887283325, 0.01938086748123169, -0.05704472213983536, -0.007208526600152254]

['Yohji Akama']

1112.2801v3

By reformulating a learning process of a set system L as a game between Teacher (presenter of data) and Learner (updater of the abstract independent set), we define the order type dim L of L to be the order type of the game tree. The theory of this new order type and continuous, monotone function between set systems corresponds to the theory of well quasi-orderings (WQOs). As Nash-Williams developed the theory of WQOs to the theory of better quasi-orderings (BQOs), we introduce a set system that has order type and corresponds to a BQO. We prove that the class of set systems corresponding to BQOs is closed by any monotone function. In (Shinohara and Arimura. "Inductive inference of unbounded unions of pattern languages from positive data." Theoretical Computer Science, pp. 191-209, 2000), for any set system L, they considered the class of arbitrary (finite) unions of members of L. From viewpoint of WQOs and BQOs, we characterize the set systems L such that the class of arbitrary (finite) unions of members of L has order type. The characterization shows that the order structure of the set system L with respect to the set-inclusion is not important for the resulting set system having order type. We point out continuous, monotone function of set systems is similar to positive reduction to Jockusch-Owings' weakly semirecursive sets.

A new order theory of set systems and better quasi-orderings

http://arxiv.org/pdf/1112.2801v3

Title new order theory set system better quasiorderings Summary reformulating learning process set system L game Teacher presenter data Learner updater abstract independent set define order type dim L L order type game tree theory new order type continuous monotone function set system corresponds theory well quasiorderings WQOs NashWilliams developed theory WQOs theory better quasiorderings BQOs introduce set system order type corresponds BQO prove class set system corresponding BQOs closed monotone function Shinohara Arimura Inductive inference unbounded union pattern language positive data Theoretical Computer Science pp 191209 2000 set system L considered class arbitrary finite union member L viewpoint WQOs BQOs characterize set system L class arbitrary finite union member L order type characterization show order structure set system L respect setinclusion important resulting set system order type point continuous monotone function set system similar positive reduction JockuschOwings weakly semirecursive set Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ziqiang Shi', 'Jiqing Han', 'Tieran Zheng', 'Shiwen Deng']

1112.4243v1

In this paper, a novel framework based on trace norm minimization for audio segment is proposed. In this framework, both the feature extraction and classification are obtained by solving corresponding convex optimization problem with trace norm regularization. For feature extraction, robust principle component analysis (robust PCA) via minimization a combination of the nuclear norm and the $\ell_1$-norm is used to extract low-rank features which are robust to white noise and gross corruption for audio segments. These low-rank features are fed to a linear classifier where the weight and bias are learned by solving similar trace norm constrained problems. For this classifier, most methods find the weight and bias in batch-mode learning, which makes them inefficient for large-scale problems. In this paper, we propose an online framework using accelerated proximal gradient method. This framework has a main advantage in memory cost. In addition, as a result of the regularization formulation of matrix classification, the Lipschitz constant was given explicitly, and hence the step size estimation of general proximal gradient method was omitted in our approach. Experiments on real data sets for laugh/non-laugh and applause/non-applause classification indicate that this novel framework is effective and noise robust.

Online Learning for Classification of Low-rank Representation Features and Its Applications in Audio Segment Classification

http://arxiv.org/pdf/1112.4243v1

Title Online Learning Classification Lowrank Representation Features Applications Audio Segment Classification Summary paper novel framework based trace norm minimization audio segment proposed framework feature extraction classification obtained solving corresponding convex optimization problem trace norm regularization feature extraction robust principle component analysis robust PCA via minimization combination nuclear norm ell1norm used extract lowrank feature robust white noise gross corruption audio segment lowrank feature fed linear classifier weight bias learned solving similar trace norm constrained problem classifier method find weight bias batchmode learning make inefficient largescale problem paper propose online framework using accelerated proximal gradient method framework main advantage memory cost addition result regularization formulation matrix classification Lipschitz constant given explicitly hence step size estimation general proximal gradient method omitted approach Experiments real data set laughnonlaugh applausenonapplause classification indicate novel framework effective noise robust Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Giovanni Zappella']

1112.4344v1

We introduce a scalable algorithm, MUCCA, for multiclass node classification in weighted graphs. Unlike previously proposed methods for the same task, MUCCA works in time linear in the number of nodes. Our approach is based on a game-theoretic formulation of the problem in which the test labels are expressed as a Nash Equilibrium of a certain game. However, in order to achieve scalability, we find the equilibrium on a spanning tree of the original graph. Experiments on real-world data reveal that MUCCA is much faster than its competitors while achieving a similar predictive performance.

A Scalable Multiclass Algorithm for Node Classification

http://arxiv.org/pdf/1112.4344v1

Title Scalable Multiclass Algorithm Node Classification Summary introduce scalable algorithm MUCCA multiclass node classification weighted graph Unlike previously proposed method task MUCCA work time linear number node approach based gametheoretic formulation problem test label expressed Nash Equilibrium certain game However order achieve scalability find equilibrium spanning tree original graph Experiments realworld data reveal MUCCA much faster competitor achieving similar predictive performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Wei Du', 'Yongjun Liao', 'and Pierre Geurts', 'Guy Leduc']

1211.0447v1

This paper addresses the large-scale acquisition of end-to-end network performance. We made two distinct contributions: ordinal rating of network performance and inference by matrix completion. The former reduces measurement costs and unifies various metrics which eases their processing in applications. The latter enables scalable and accurate inference with no requirement of structural information of the network nor geometric constraints. By combining both, the acquisition problem bears strong similarities to recommender systems. This paper investigates the applicability of various matrix factorization models used in recommender systems. We found that the simple regularized matrix factorization is not only practical but also produces accurate results that are beneficial for peer selection.

Ordinal Rating of Network Performance and Inference by Matrix Completion

http://arxiv.org/pdf/1211.0447v1

Title Ordinal Rating Network Performance Inference Matrix Completion Summary paper address largescale acquisition endtoend network performance made two distinct contribution ordinal rating network performance inference matrix completion former reduces measurement cost unifies various metric eas processing application latter enables scalable accurate inference requirement structural information network geometric constraint combining acquisition problem bear strong similarity recommender system paper investigates applicability various matrix factorization model used recommender system found simple regularized matrix factorization practical also produce accurate result beneficial peer selection Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Amit Daniely', 'Nati Linial', 'Shai Shalev-Shwartz']

1211.0616v4

Many popular learning algorithms (E.g. Regression, Fourier-Transform based algorithms, Kernel SVM and Kernel ridge regression) operate by reducing the problem to a convex optimization problem over a vector space of functions. These methods offer the currently best approach to several central problems such as learning half spaces and learning DNF's. In addition they are widely used in numerous application domains. Despite their importance, there are still very few proof techniques to show limits on the power of these algorithms. We study the performance of this approach in the problem of (agnostically and improperly) learning halfspaces with margin $\gamma$. Let $\mathcal{D}$ be a distribution over labeled examples. The $\gamma$-margin error of a hyperplane $h$ is the probability of an example to fall on the wrong side of $h$ or at a distance $\le\gamma$ from it. The $\gamma$-margin error of the best $h$ is denoted $\mathrm{Err}_\gamma(\mathcal{D})$. An $\alpha(\gamma)$-approximation algorithm receives $\gamma,\epsilon$ as input and, using i.i.d. samples of $\mathcal{D}$, outputs a classifier with error rate $\le \alpha(\gamma)\mathrm{Err}_\gamma(\mathcal{D}) + \epsilon$. Such an algorithm is efficient if it uses $\mathrm{poly}(\frac{1}{\gamma},\frac{1}{\epsilon})$ samples and runs in time polynomial in the sample size. The best approximation ratio achievable by an efficient algorithm is $O\left(\frac{1/\gamma}{\sqrt{\log(1/\gamma)}}\right)$ and is achieved using an algorithm from the above class. Our main result shows that the approximation ratio of every efficient algorithm from this family must be $\ge \Omega\left(\frac{1/\gamma}{\mathrm{poly}\left(\log\left(1/\gamma\right)\right)}\right)$, essentially matching the best known upper bound.

The complexity of learning halfspaces using generalized linear methods

http://arxiv.org/pdf/1211.0616v4

Title complexity learning halfspaces using generalized linear method Summary Many popular learning algorithm Eg Regression FourierTransform based algorithm Kernel SVM Kernel ridge regression operate reducing problem convex optimization problem vector space function method offer currently best approach several central problem learning half space learning DNFs addition widely used numerous application domain Despite importance still proof technique show limit power algorithm study performance approach problem agnostically improperly learning halfspaces margin gamma Let mathcalD distribution labeled example gammamargin error hyperplane h probability example fall wrong side h distance legamma gammamargin error best h denoted mathrmErrgammamathcalD alphagammaapproximation algorithm receives gammaepsilon input using iid sample mathcalD output classifier error rate le alphagammamathrmErrgammamathcalD epsilon algorithm efficient us mathrmpolyfrac1gammafrac1epsilon sample run time polynomial sample size best approximation ratio achievable efficient algorithm Oleftfrac1gammasqrtlog1gammaright achieved using algorithm class main result show approximation ratio every efficient algorithm family must ge Omegaleftfrac1gammamathrmpolyleftlogleft1gammarightrightright essentially matching best known upper bound Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Xiaofei Wang', 'Mingming Zhang', 'Liyong Shen', 'Suixiang Gao']

1211.1526v2

The prevention of dangerous chemical accidents is a primary problem of industrial manufacturing. In the accidents of dangerous chemicals, the oil gas explosion plays an important role. The essential task of the explosion prevention is to estimate the better explosion limit of a given oil gas. In this paper, Support Vector Machines (SVM) and Logistic Regression (LR) are used to predict the explosion of oil gas. LR can get the explicit probability formula of explosion, and the explosive range of the concentrations of oil gas according to the concentration of oxygen. Meanwhile, SVM gives higher accuracy of prediction. Furthermore, considering the practical requirements, the effects of penalty parameter on the distribution of two types of errors are discussed.

Explosion prediction of oil gas using SVM and Logistic Regression

http://arxiv.org/pdf/1211.1526v2

Title Explosion prediction oil gas using SVM Logistic Regression Summary prevention dangerous chemical accident primary problem industrial manufacturing accident dangerous chemical oil gas explosion play important role essential task explosion prevention estimate better explosion limit given oil gas paper Support Vector Machines SVM Logistic Regression LR used predict explosion oil gas LR get explicit probability formula explosion explosive range concentration oil gas according concentration oxygen Meanwhile SVM give higher accuracy prediction Furthermore considering practical requirement effect penalty parameter distribution two type error discussed Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['H. Brendan McMahan', 'Omkar Muralidharan']

1211.3955v1

Calibration is a basic property for prediction systems, and algorithms for achieving it are well-studied in both statistics and machine learning. In many applications, however, the predictions are used to make decisions that select which observations are made. This makes calibration difficult, as adjusting predictions to achieve calibration changes future data. We focus on click-through-rate (CTR) prediction for search ad auctions. Here, CTR predictions are used by an auction that determines which ads are shown, and we want to maximize the value generated by the auction. We show that certain natural notions of calibration can be impossible to achieve, depending on the details of the auction. We also show that it can be impossible to maximize auction efficiency while using calibrated predictions. Finally, we give conditions under which calibration is achievable and simultaneously maximizes auction efficiency: roughly speaking, bids and queries must not contain information about CTRs that is not already captured by the predictions.

On Calibrated Predictions for Auction Selection Mechanisms

http://arxiv.org/pdf/1211.3955v1

Title Calibrated Predictions Auction Selection Mechanisms Summary Calibration basic property prediction system algorithm achieving wellstudied statistic machine learning many application however prediction used make decision select observation made make calibration difficult adjusting prediction achieve calibration change future data focus clickthroughrate CTR prediction search ad auction CTR prediction used auction determines ad shown want maximize value generated auction show certain natural notion calibration impossible achieve depending detail auction also show impossible maximize auction efficiency using calibrated prediction Finally give condition calibration achievable simultaneously maximizes auction efficiency roughly speaking bid query must contain information CTRs already captured prediction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Frédéric Bastien', 'Pascal Lamblin', 'Razvan Pascanu', 'James Bergstra', 'Ian Goodfellow', 'Arnaud Bergeron', 'Nicolas Bouchard', 'David Warde-Farley', 'Yoshua Bengio']

1211.5590v1

Theano is a linear algebra compiler that optimizes a user's symbolically-specified mathematical computations to produce efficient low-level implementations. In this paper, we present new features and efficiency improvements to Theano, and benchmarks demonstrating Theano's performance relative to Torch7, a recently introduced machine learning library, and to RNNLM, a C++ library targeted at recurrent neural networks.

Theano: new features and speed improvements

http://arxiv.org/pdf/1211.5590v1

Title Theano new feature speed improvement Summary Theano linear algebra compiler optimizes user symbolicallyspecified mathematical computation produce efficient lowlevel implementation paper present new feature efficiency improvement Theano benchmark demonstrating Theanos performance relative Torch7 recently introduced machine learning library RNNLM C library targeted recurrent neural network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mehrdad Mahdavi', 'Tianbao Yang', 'Rong Jin']

1211.6013v2

In this paper we propose a general framework to characterize and solve the stochastic optimization problems with multiple objectives underlying many real world learning applications. We first propose a projection based algorithm which attains an $O(T^{-1/3})$ convergence rate. Then, by leveraging on the theory of Lagrangian in constrained optimization, we devise a novel primal-dual stochastic approximation algorithm which attains the optimal convergence rate of $O(T^{-1/2})$ for general Lipschitz continuous objectives.

Online Stochastic Optimization with Multiple Objectives

http://arxiv.org/pdf/1211.6013v2

Title Online Stochastic Optimization Multiple Objectives Summary paper propose general framework characterize solve stochastic optimization problem multiple objective underlying many real world learning application first propose projection based algorithm attains OT13 convergence rate leveraging theory Lagrangian constrained optimization devise novel primaldual stochastic approximation algorithm attains optimal convergence rate OT12 general Lipschitz continuous objective Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Patrick Rebentrost', 'Masoud Mohseni', 'Seth Lloyd']

1307.0471v3

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases when classical sampling algorithms require polynomial time, an exponential speed-up is obtained. At the core of this quantum big data algorithm is a non-sparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

Quantum support vector machine for big data classification

http://arxiv.org/pdf/1307.0471v3

Title Quantum support vector machine big data classification Summary Supervised machine learning classification new data based already classified training example work show support vector machine optimized binary classifier implemented quantum computer complexity logarithmic size vector number training example case classical sampling algorithm require polynomial time exponential speedup obtained core quantum big data algorithm nonsparse matrix exponentiation technique efficiently performing matrix inversion training data innerproduct kernel matrix Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jenna Reps', 'Jan Feyereisl', 'Jonathan M. Garibaldi', 'Uwe Aickelin', 'Jack E. Gibson', 'Richard B. Hubbard']

1307.1078v1

Data-mining techniques have frequently been developed for Spontaneous reporting databases. These techniques aim to find adverse drug events accurately and efficiently. Spontaneous reporting databases are prone to missing information, under reporting and incorrect entries. This often results in a detection lag or prevents the detection of some adverse drug events. These limitations do not occur in electronic health-care databases. In this paper, existing methods developed for spontaneous reporting databases are implemented on both a spontaneous reporting database and a general practice electronic health-care database and compared. The results suggests that the application of existing methods to the general practice database may help find signals that have gone undetected when using the spontaneous reporting system database. In addition the general practice database provides far more supplementary information, that if incorporated in analysis could provide a wealth of information for identifying adverse events more accurately.

Investigating the Detection of Adverse Drug Events in a UK General Practice Electronic Health-Care Database

http://arxiv.org/pdf/1307.1078v1

Title Investigating Detection Adverse Drug Events UK General Practice Electronic HealthCare Database Summary Datamining technique frequently developed Spontaneous reporting database technique aim find adverse drug event accurately efficiently Spontaneous reporting database prone missing information reporting incorrect entry often result detection lag prevents detection adverse drug event limitation occur electronic healthcare database paper existing method developed spontaneous reporting database implemented spontaneous reporting database general practice electronic healthcare database compared result suggests application existing method general practice database may help find signal gone undetected using spontaneous reporting system database addition general practice database provides far supplementary information incorporated analysis could provide wealth information identifying adverse event accurately Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ian Dent', 'Uwe Aickelin', 'Tom Rodden']

1307.1079v1

This paper takes an approach to clustering domestic electricity load profiles that has been successfully used with data from Portugal and applies it to UK data. Clustering techniques are applied and it is found that the preferred technique in the Portuguese work (a two stage process combining Self Organised Maps and Kmeans) is not appropriate for the UK data. The work shows that up to nine clusters of households can be identified with the differences in usage profiles being visually striking. This demonstrates the appropriateness of breaking the electricity usage patterns down to more detail than the two load profiles currently published by the electricity industry. The paper details initial results using data collected in Milton Keynes around 1990. Further work is described and will concentrate on building accurate and meaningful clusters of similar electricity users in order to better direct demand side management initiatives to the most relevant target customers.

Application of a clustering framework to UK domestic electricity data

http://arxiv.org/pdf/1307.1079v1

Title Application clustering framework UK domestic electricity data Summary paper take approach clustering domestic electricity load profile successfully used data Portugal applies UK data Clustering technique applied found preferred technique Portuguese work two stage process combining Self Organised Maps Kmeans appropriate UK data work show nine cluster household identified difference usage profile visually striking demonstrates appropriateness breaking electricity usage pattern detail two load profile currently published electricity industry paper detail initial result using data collected Milton Keynes around 1990 work described concentrate building accurate meaningful cluster similar electricity user order better direct demand side management initiative relevant target customer Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ian Dent', 'Christian Wagner', 'Uwe Aickelin', 'Tom Rodden']

1307.1385v1

Changes in the UK electricity market mean that domestic users will be required to modify their usage behaviour in order that supplies can be maintained. Clustering allows usage profiles collected at the household level to be clustered into groups and assigned a stereotypical profile which can be used to target marketing campaigns. Fuzzy C Means clustering extends this by allowing each household to be a member of many groups and hence provides the opportunity to make personalised offers to the household dependent on their degree of membership of each group. In addition, feedback can be provided on how user's changing behaviour is moving them towards more "green" or cost effective stereotypical usage.

Creating Personalised Energy Plans. From Groups to Individuals using Fuzzy C Means Clustering

http://arxiv.org/pdf/1307.1385v1

Title Creating Personalised Energy Plans Groups Individuals using Fuzzy C Means Clustering Summary Changes UK electricity market mean domestic user required modify usage behaviour order supply maintained Clustering allows usage profile collected household level clustered group assigned stereotypical profile used target marketing campaign Fuzzy C Means clustering extends allowing household member many group hence provides opportunity make personalised offer household dependent degree membership group addition feedback provided user changing behaviour moving towards green cost effective stereotypical usage Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hala Helmi', 'Jon M. Garibaldi', 'Uwe Aickelin']

1307.1387v1

Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function properly when labelled data (training examples) are insufficient using Support Vector Machines (SVM) algorithms. Therefore, in this paper, we suggest Transductive Support Vector Machines (TSVMs) as semi-supervised learning algorithms, learning with both labelled samples data and unlabelled samples to perform the classification of microarray data. To prune the superfluous genes and samples we used a feature selection method called Recursive Feature Elimination (RFE), which is supposed to enhance the output of classification and avoid the local optimization problem. We examined the classification prediction accuracy of the TSVM-RFE algorithm in comparison with the Genetic Learning Across Datasets (GLAD) algorithm, as both are semi-supervised learning methods. Comparing these two methods, we found that the TSVM-RFE surpassed both a SVM using RFE and GLAD.

Examining the Classification Accuracy of TSVMs with ?Feature Selection in Comparison with the GLAD Algorithm

http://arxiv.org/pdf/1307.1387v1

Title Examining Classification Accuracy TSVMs Feature Selection Comparison GLAD Algorithm Summary Gene expression data set used classify predict patient diagnostic category know extremely difficult expensive obtain gene expression labelled example Moreover conventional supervised approach cannot function properly labelled data training example insufficient using Support Vector Machines SVM algorithm Therefore paper suggest Transductive Support Vector Machines TSVMs semisupervised learning algorithm learning labelled sample data unlabelled sample perform classification microarray data prune superfluous gene sample used feature selection method called Recursive Feature Elimination RFE supposed enhance output classification avoid local optimization problem examined classification prediction accuracy TSVMRFE algorithm comparison Genetic Learning Across Datasets GLAD algorithm semisupervised learning method Comparing two method found TSVMRFE surpassed SVM using RFE GLAD Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Feng Gu', 'Jan Feyereisl', 'Robert Oates', 'Jenna Reps', 'Julie Greensmith', 'Uwe Aickelin']

1307.1391v1

Theoretical analyses of the Dendritic Cell Algorithm (DCA) have yielded several criticisms about its underlying structure and operation. As a result, several alterations and fixes have been suggested in the literature to correct for these findings. A contribution of this work is to investigate the effects of replacing the classification stage of the DCA (which is known to be flawed) with a traditional machine learning technique. This work goes on to question the merits of those unique properties of the DCA that are yet to be thoroughly analysed. If none of these properties can be found to have a benefit over traditional approaches, then "fixing" the DCA is arguably less efficient than simply creating a new algorithm. This work examines the dynamic filtering property of the DCA and questions the utility of this unique feature for the anomaly detection problem. It is found that this feature, while advantageous for noisy, time-ordered classification, is not as useful as a traditional static filter for processing a synthetic dataset. It is concluded that there are still unique features of the DCA left to investigate. Areas that may be of benefit to the Artificial Immune Systems community are suggested.

Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm

http://arxiv.org/pdf/1307.1391v1

Title Quiet Class Classification Noise Dendritic Cell Algorithm Summary Theoretical analysis Dendritic Cell Algorithm DCA yielded several criticism underlying structure operation result several alteration fix suggested literature correct finding contribution work investigate effect replacing classification stage DCA known flawed traditional machine learning technique work go question merit unique property DCA yet thoroughly analysed none property found benefit traditional approach fixing DCA arguably le efficient simply creating new algorithm work examines dynamic filtering property DCA question utility unique feature anomaly detection problem found feature advantageous noisy timeordered classification useful traditional static filter processing synthetic dataset concluded still unique feature DCA left investigate Areas may benefit Artificial Immune Systems community suggested Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yihui Liu', 'Uwe Aickelin']

1307.1394v1

Adverse drug reaction (ADR) is widely concerned for public health issue. In this study we propose an original approach to detect the ADRs using feature matrix and feature selection. The experiments are carried out on the drug Simvastatin. Major side effects for the drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on the computerized method, further investigation is needed.

Detect adverse drug reactions for drug Alendronate

http://arxiv.org/pdf/1307.1394v1

Title Detect adverse drug reaction drug Alendronate Summary Adverse drug reaction ADR widely concerned public health issue study propose original approach detect ADRs using feature matrix feature selection experiment carried drug Simvastatin Major side effect drug detected better performance achieved compared computerized method detected ADRs based computerized method investigation needed Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Chris Roadknight', 'Uwe Aickelin', 'Alex Ladas', 'Daniele Soria', 'John Scholefield', 'Lindy Durrant']

1307.1601v1

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to cluster this dataset and important subsets of it in an effort to characterize the data and validate existing standards for tumour classification. It is apparent from optimal clustering that existing tumour classification is largely unrelated to immunological factors within a patient and that there may be scope for re-evaluating treatment options and survival estimates based on a combination of tumour physiology and patient histochemistry.

Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification

http://arxiv.org/pdf/1307.1601v1

Title Biomarker Clustering Colorectal Cancer Data Complement Clinical Classification Summary paper describe dataset relating cellular physical condition patient operated upon remove colorectal tumour data provides unique insight immunological status point tumour removal tumour classification postoperative survival Attempts made cluster dataset important subset effort characterize data validate existing standard tumour classification apparent optimal clustering existing tumour classification largely unrelated immunological factor within patient may scope reevaluating treatment option survival estimate based combination tumour physiology patient histochemistry Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Wei Chen', 'Dayu Huang', 'Ankur A. Kulkarni', 'Jayakrishnan Unnikrishnan', 'Quanyan Zhu', 'Prashant Mehta', 'Sean Meyn', 'Adam Wierman']

1307.1759v2

Neuro-dynamic programming is a class of powerful techniques for approximating the solution to dynamic programming equations. In their most computationally attractive formulations, these techniques provide the approximate solution only within a prescribed finite-dimensional function class. Thus, the question that always arises is how should the function class be chosen? The goal of this paper is to propose an approach using the solutions to associated fluid and diffusion approximations. In order to illustrate this approach, the paper focuses on an application to dynamic speed scaling for power management in computer processors.

Approximate dynamic programming using fluid and diffusion approximations with applications to power management

http://arxiv.org/pdf/1307.1759v2

Title Approximate dynamic programming using fluid diffusion approximation application power management Summary Neurodynamic programming class powerful technique approximating solution dynamic programming equation computationally attractive formulation technique provide approximate solution within prescribed finitedimensional function class Thus question always arises function class chosen goal paper propose approach using solution associated fluid diffusion approximation order illustrate approach paper focus application dynamic speed scaling power management computer processor Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Alexandros Ladas', 'Uwe Aickelin', 'Jon Garibaldi', 'Eamonn Ferguson']

1307.1998v1

It has become apparent that models that have been applied widely in economics, including Machine Learning techniques and Data Mining methods, should take into consideration principles that derive from the theories of Personality Psychology in order to discover more comprehensive knowledge regarding complicated economic behaviours. In this work, we present a method to extract Behavioural Groups by using simple clustering techniques that can potentially reveal aspects of the Personalities for their members. We believe that this is very important because the psychological information regarding the Personalities of individuals is limited in real world applications and because it can become a useful tool in improving the traditional models of Knowledge Economy.

Using Clustering to extract Personality Information from socio economic data

http://arxiv.org/pdf/1307.1998v1

Title Using Clustering extract Personality Information socio economic data Summary become apparent model applied widely economics including Machine Learning technique Data Mining method take consideration principle derive theory Personality Psychology order discover comprehensive knowledge regarding complicated economic behaviour work present method extract Behavioural Groups using simple clustering technique potentially reveal aspect Personalities member believe important psychological information regarding Personalities individual limited real world application become useful tool improving traditional model Knowledge Economy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ian Dent', 'Tony Craig', 'Uwe Aickelin', 'Tom Rodden']

1307.2111v1

Changes in the UK electricity market, particularly with the roll out of smart meters, will provide greatly increased opportunities for initiatives intended to change households' electricity usage patterns for the benefit of the overall system. Users show differences in their regular behaviours and clustering households into similar groupings based on this variability provides for efficient targeting of initiatives. Those people who are stuck into a regular pattern of activity may be the least receptive to an initiative to change behaviour. A sample of 180 households from the UK are clustered into four groups as an initial test of the concept and useful, actionable groupings are found.

Finding the creatures of habit; Clustering households based on their flexibility in using electricity

http://arxiv.org/pdf/1307.2111v1

Title Finding creature habit Clustering household based flexibility using electricity Summary Changes UK electricity market particularly roll smart meter provide greatly increased opportunity initiative intended change household electricity usage pattern benefit overall system Users show difference regular behaviour clustering household similar grouping based variability provides efficient targeting initiative people stuck regular pattern activity may least receptive initiative change behaviour sample 180 household UK clustered four group initial test concept useful actionable grouping found Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['T. Chandrasekhar', 'K. Thangavel', 'E. Elayaraja', 'E. N. Sathishkumar']

1307.3337v1

Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. Identification of co-expressed genes and coherent patterns is the central goal in microarray or gene expression data analysis and is an important task in bioinformatics research. Feature selection is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this work the unsupervised Gene selection method and Enhanced Center Initialization Algorithm (ECIA) with K-Means algorithms have been applied for clustering of Gene Expression Data. This proposed clustering algorithm overcomes the drawbacks in terms of specifying the optimal number of clusters and initialization of good cluster centroids. Gene Expression Data show that could identify compact clusters with performs well in terms of the Silhouette Coefficients cluster measure.

Unsupervised Gene Expression Data using Enhanced Clustering Method

http://arxiv.org/pdf/1307.3337v1

Title Unsupervised Gene Expression Data using Enhanced Clustering Method Summary Microarrays made possible simultaneously monitor expression profile thousand gene various experimental condition Identification coexpressed gene coherent pattern central goal microarray gene expression data analysis important task bioinformatics research Feature selection process select feature informative one important step knowledge discovery problem feature important feature may redundant others may irrelevant noisy work unsupervised Gene selection method Enhanced Center Initialization Algorithm ECIA KMeans algorithm applied clustering Gene Expression Data proposed clustering algorithm overcomes drawback term specifying optimal number cluster initialization good cluster centroid Gene Expression Data show could identify compact cluster performs well term Silhouette Coefficients cluster measure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['T. Chandrasekhar', 'K. Thangavel', 'E. Elayaraja']

1307.3549v1

Microarray technology is a process that allows thousands of genes simultaneously monitor to various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins, This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this paper we analysed K-Means with Automatic Generations of Merge Factor for ISODATA- AGMFI, to group the microarray data sets on the basic of ISODATA. AGMFI is to generate initial values for merge and Spilt factor, maximum merge times instead of selecting efficient values as in ISODATA. The initial seeds for each cluster were normally chosen either sequentially or randomly. The quality of the final clusters was found to be influenced by these initial seeds. For the real life problems, the suitable number of clusters cannot be predicted. To overcome the above drawback the current research focused on developing the clustering algorithms without giving the initial number of clusters.

Performance Analysis of Clustering Algorithms for Gene Expression Data

http://arxiv.org/pdf/1307.3549v1

Title Performance Analysis Clustering Algorithms Gene Expression Data Summary Microarray technology process allows thousand gene simultaneously monitor various experimental condition used identify coexpressed gene specific cell tissue actively used make protein method used analysis gene expression important task bioinformatics research Cluster analysis gene expression data proved useful tool identifying coexpressed gene biologically relevant grouping gene sample paper analysed KMeans Automatic Generations Merge Factor ISODATA AGMFI group microarray data set basic ISODATA AGMFI generate initial value merge Spilt factor maximum merge time instead selecting efficient value ISODATA initial seed cluster normally chosen either sequentially randomly quality final cluster found influenced initial seed real life problem suitable number cluster cannot predicted overcome drawback current research focused developing clustering algorithm without giving initial number cluster Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Alexandros Ntoulas', 'Omar Alonso', 'Vasilis Kandylas']

1307.3673v1

As the number of applications that use machine learning algorithms increases, the need for labeled data useful for training such algorithms intensifies. Getting labels typically involves employing humans to do the annotation, which directly translates to training and working costs. Crowdsourcing platforms have made labeling cheaper and faster, but they still involve significant costs, especially for the cases where the potential set of candidate data to be labeled is large. In this paper we describe a methodology and a prototype system aiming at addressing this challenge for Web-scale problems in an industrial setting. We discuss ideas on how to efficiently select the data to use for training of machine learning algorithms in an attempt to reduce cost. We show results achieving good performance with reduced cost by carefully selecting which instances to label. Our proposed algorithm is presented as part of a framework for managing and generating training datasets, which includes, among other components, a human computation element.

A Data Management Approach for Dataset Selection Using Human Computation

http://arxiv.org/pdf/1307.3673v1

Title Data Management Approach Dataset Selection Using Human Computation Summary number application use machine learning algorithm increase need labeled data useful training algorithm intensifies Getting label typically involves employing human annotation directly translates training working cost Crowdsourcing platform made labeling cheaper faster still involve significant cost especially case potential set candidate data labeled large paper describe methodology prototype system aiming addressing challenge Webscale problem industrial setting discus idea efficiently select data use training machine learning algorithm attempt reduce cost show result achieving good performance reduced cost carefully selecting instance label proposed algorithm presented part framework managing generating training datasets includes among component human computation element Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Junzhou Zhao']

1307.3687v1

Constrained connection is the phenomenon that a reviewer can only review a subset of products/services due to narrow range of interests or limited attention capacity. In this work, we study how constrained connections can affect estimation performance in online review systems (ORS). We find that reviewers' constrained connections will cause poor estimation performance, both from the measurements of estimation accuracy and Bayesian Cramer Rao lower bound.

On Analyzing Estimation Errors due to Constrained Connections in Online Review Systems

http://arxiv.org/pdf/1307.3687v1

Title Analyzing Estimation Errors due Constrained Connections Online Review Systems Summary Constrained connection phenomenon reviewer review subset productsservices due narrow range interest limited attention capacity work study constrained connection affect estimation performance online review system ORS find reviewer constrained connection cause poor estimation performance measurement estimation accuracy Bayesian Cramer Rao lower bound Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Khalid Raza', 'Atif N Hasan']

1307.7050v1

Prostate cancer is among the most common cancer in males and its heterogeneity is well known. Its early detection helps making therapeutic decision. There is no standard technique or procedure yet which is full-proof in predicting cancer class. The genomic level changes can be detected in gene expression data and those changes may serve as standard model for any random cancer data for class prediction. Various techniques were implied on prostate cancer data set in order to accurately predict cancer class including machine learning techniques. Huge number of attributes and few number of sample in microarray data leads to poor machine learning, therefore the most challenging part is attribute reduction or non significant gene reduction. In this work we have compared several machine learning techniques for their accuracy in predicting the cancer class. Machine learning is effective when number of attributes (genes) are larger than the number of samples which is rarely possible with gene expression data. Attribute reduction or gene filtering is absolutely required in order to make the data more meaningful as most of the genes do not participate in tumor development and are irrelevant for cancer prediction. Here we have applied combination of statistical techniques such as inter-quartile range and t-test, which has been effective in filtering significant genes and minimizing noise from data. Further we have done a comprehensive evaluation of ten state-of-the-art machine learning techniques for their accuracy in class prediction of prostate cancer. Out of these techniques, Bayes Network out performed with an accuracy of 94.11% followed by Navie Bayes with an accuracy of 91.17%. To cross validate our results, we modified our training dataset in six different way and found that average sensitivity, specificity, precision and accuracy of Bayes Network is highest among all other techniques used.

A Comprehensive Evaluation of Machine Learning Techniques for Cancer Class Prediction Based on Microarray Data

http://arxiv.org/pdf/1307.7050v1

Title Comprehensive Evaluation Machine Learning Techniques Cancer Class Prediction Based Microarray Data Summary Prostate cancer among common cancer male heterogeneity well known early detection help making therapeutic decision standard technique procedure yet fullproof predicting cancer class genomic level change detected gene expression data change may serve standard model random cancer data class prediction Various technique implied prostate cancer data set order accurately predict cancer class including machine learning technique Huge number attribute number sample microarray data lead poor machine learning therefore challenging part attribute reduction non significant gene reduction work compared several machine learning technique accuracy predicting cancer class Machine learning effective number attribute gene larger number sample rarely possible gene expression data Attribute reduction gene filtering absolutely required order make data meaningful gene participate tumor development irrelevant cancer prediction applied combination statistical technique interquartile range ttest effective filtering significant gene minimizing noise data done comprehensive evaluation ten stateoftheart machine learning technique accuracy class prediction prostate cancer technique Bayes Network performed accuracy 9411 followed Navie Bayes accuracy 9117 cross validate result modified training dataset six different way found average sensitivity specificity precision accuracy Bayes Network highest among technique used Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mehrdad Mahdavi', 'Rong Jin']

1307.7192v1

It is well known that the optimal convergence rate for stochastic optimization of smooth functions is $O(1/\sqrt{T})$, which is same as stochastic optimization of Lipschitz continuous convex functions. This is in contrast to optimizing smooth functions using full gradients, which yields a convergence rate of $O(1/T^2)$. In this work, we consider a new setup for optimizing smooth functions, termed as {\bf Mixed Optimization}, which allows to access both a stochastic oracle and a full gradient oracle. Our goal is to significantly improve the convergence rate of stochastic optimization of smooth functions by having an additional small number of accesses to the full gradient oracle. We show that, with an $O(\ln T)$ calls to the full gradient oracle and an $O(T)$ calls to the stochastic oracle, the proposed mixed optimization algorithm is able to achieve an optimization error of $O(1/T)$.

MixedGrad: An O(1/T) Convergence Rate Algorithm for Stochastic Smooth Optimization

http://arxiv.org/pdf/1307.7192v1

Title MixedGrad O1T Convergence Rate Algorithm Stochastic Smooth Optimization Summary well known optimal convergence rate stochastic optimization smooth function O1sqrtT stochastic optimization Lipschitz continuous convex function contrast optimizing smooth function using full gradient yield convergence rate O1T2 work consider new setup optimizing smooth function termed bf Mixed Optimization allows access stochastic oracle full gradient oracle goal significantly improve convergence rate stochastic optimization smooth function additional small number access full gradient oracle show Oln call full gradient oracle OT call stochastic oracle proposed mixed optimization algorithm able achieve optimization error O1T Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Harjinder Kaur', 'Gurpreet Singh', 'Jaspreet Minhas']

1307.7286v1

Intrusion detection is so much popular since the last two decades where intrusion is attempted to break into or misuse the system. It is mainly of two types based on the intrusions, first is Misuse or signature based detection and the other is Anomaly detection. In this paper Machine learning based methods which are one of the types of Anomaly detection techniques is discussed.

A Review of Machine Learning based Anomaly Detection Techniques

http://arxiv.org/pdf/1307.7286v1

Title Review Machine Learning based Anomaly Detection Techniques Summary Intrusion detection much popular since last two decade intrusion attempted break misuse system mainly two type based intrusion first Misuse signature based detection Anomaly detection paper Machine learning based method one type Anomaly detection technique discussed Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Amin Babazadeh Sangar', 'Seyyed Reza Khaze', 'Laya Ebrahimi']

1307.7429v1

Anticipating the political behavior of people will be considerable help for election candidates to assess the possibility of their success and to be acknowledged about the public motivations to select them. In this paper, we provide a general schematic of the architecture of participation anticipating system in presidential election by using KNN, Classification Tree and Na\"ive Bayes and tools orange based on crisp which had hopeful output. To test and assess the proposed model, we begin to use the case study by selecting 100 qualified persons who attend in 11th presidential election of Islamic republic of Iran and anticipate their participation in Kohkiloye & Boyerahmad. We indicate that KNN can perform anticipation and classification processes with high accuracy in compared with two other algorithms to anticipate participation.

Participation anticipating in elections using data mining methods

http://arxiv.org/pdf/1307.7429v1

Title Participation anticipating election using data mining method Summary Anticipating political behavior people considerable help election candidate ass possibility success acknowledged public motivation select paper provide general schematic architecture participation anticipating system presidential election using KNN Classification Tree Naive Bayes tool orange based crisp hopeful output test ass proposed model begin use case study selecting 100 qualified person attend 11th presidential election Islamic republic Iran anticipate participation Kohkiloye Boyerahmad indicate KNN perform anticipation classification process high accuracy compared two algorithm anticipate participation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Farhad Soleimanian Gharehchopogh', 'Seyyed Reza Khaze']

1307.7432v1

Blogs are the recent emerging media which relies on information technology and technological advance. Since the mass media in some less-developed and developing countries are in government service and their policies are developed based on governmental interests, so blogs are provided for ideas and exchanging opinions. In this paper, we highlighted performed simulations from obtained information from 100 users and bloggers in Kohkiloye and Boyer Ahmad Province and using Weka 3.6 tool and c4.5 algorithm by applying decision tree with more than %82 precision for getting future tendency anticipation of users to blogging and using in strategically areas.

Data mining application for cyber space users tendency in blog writing: a case study

http://arxiv.org/pdf/1307.7432v1

Title Data mining application cyber space user tendency blog writing case study Summary Blogs recent emerging medium relies information technology technological advance Since mass medium lessdeveloped developing country government service policy developed based governmental interest blog provided idea exchanging opinion paper highlighted performed simulation obtained information 100 user blogger Kohkiloye Boyer Ahmad Province using Weka 36 tool c45 algorithm applying decision tree 82 precision getting future tendency anticipation user blogging using strategically area Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['R. J. Lyon', 'J. M. Brooke', 'J. D. Knowles', 'B. W. Stappers']

1307.8012v1

The domain of radio astronomy is currently facing significant computational challenges, foremost amongst which are those posed by the development of the world's largest radio telescope, the Square Kilometre Array (SKA). Preliminary specifications for this instrument suggest that the final design will incorporate between 2000 and 3000 individual 15 metre receiving dishes, which together can be expected to produce a data rate of many TB/s. Given such a high data rate, it becomes crucial to consider how this information will be processed and stored to maximise its scientific utility. In this paper, we consider one possible data processing scenario for the SKA, for the purposes of an all-sky pulsar survey. In particular we treat the selection of promising signals from the SKA processing pipeline as a data stream classification problem. We consider the feasibility of classifying signals that arrive via an unlabelled and heavily class imbalanced data stream, using currently available algorithms and frameworks. Our results indicate that existing stream learners exhibit unacceptably low recall on real astronomical data when used in standard configuration; however, good false positive performance and comparable accuracy to static learners, suggests they have definite potential as an on-line solution to this particular big data challenge.

A Study on Classification in Imbalanced and Partially-Labelled Data Streams

http://arxiv.org/pdf/1307.8012v1

Title Study Classification Imbalanced PartiallyLabelled Data Streams Summary domain radio astronomy currently facing significant computational challenge foremost amongst posed development world largest radio telescope Square Kilometre Array SKA Preliminary specification instrument suggest final design incorporate 2000 3000 individual 15 metre receiving dish together expected produce data rate many TBs Given high data rate becomes crucial consider information processed stored maximise scientific utility paper consider one possible data processing scenario SKA purpose allsky pulsar survey particular treat selection promising signal SKA processing pipeline data stream classification problem consider feasibility classifying signal arrive via unlabelled heavily class imbalanced data stream using currently available algorithm framework result indicate existing stream learner exhibit unacceptably low recall real astronomical data used standard configuration however good false positive performance comparable accuracy static learner suggests definite potential online solution particular big data challenge Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Fayao Liu', 'Luping Zhou', 'Chunhua Shen', 'Jianping Yin']

1310.0890v1

To achieve effective and efficient detection of Alzheimer's disease (AD), many machine learning methods have been introduced into this realm. However, the general case of limited training samples, as well as different feature representations typically makes this problem challenging. In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement. Contrary to the usual way of solving the problem in the dual space, we look at the optimization from a new perspective. By conducting Fourier transform on the Gaussian kernel, we explicitly compute the mapping function, which leads to a more straightforward solution of the problem in the primal space. Furthermore, we impose the mixed $L_{21}$ norm constraint on the kernel weights, known as the group lasso regularization, to enforce group sparsity among different feature modalities. This actually acts as a role of feature modality selection, while at the same time exploiting complementary information among different kernels. Therefore it is able to extract the most discriminative features for classification. Experiments on the ADNI data set demonstrate the effectiveness of the proposed method.

Multiple Kernel Learning in the Primal for Multi-modal Alzheimer's Disease Classification

http://arxiv.org/pdf/1310.0890v1

Title Multiple Kernel Learning Primal Multimodal Alzheimers Disease Classification Summary achieve effective efficient detection Alzheimers disease AD many machine learning method introduced realm However general case limited training sample well different feature representation typically make problem challenging work propose novel multiple kernel learning framework combine multimodal feature AD classification scalable easy implement Contrary usual way solving problem dual space look optimization new perspective conducting Fourier transform Gaussian kernel explicitly compute mapping function lead straightforward solution problem primal space Furthermore impose mixed L21 norm constraint kernel weight known group lasso regularization enforce group sparsity among different feature modality actually act role feature modality selection time exploiting complementary information among different kernel Therefore able extract discriminative feature classification Experiments ADNI data set demonstrate effectiveness proposed method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dan He', 'Irina Rish', 'David Haws', 'Simon Teyssedre', 'Zivan Karaman', 'Laxmi Parida']

1310.1659v1

Whole genome prediction of complex phenotypic traits using high-density genotyping arrays has attracted a great deal of attention, as it is relevant to the fields of plant and animal breeding and genetic epidemiology. As the number of genotypes is generally much bigger than the number of samples, predictive models suffer from the curse-of-dimensionality. The curse-of-dimensionality problem not only affects the computational efficiency of a particular genomic selection method, but can also lead to poor performance, mainly due to correlation among markers. In this work we proposed the first transductive feature selection method based on the MRMR (Max-Relevance and Min-Redundancy) criterion which we call MINT. We applied MINT on genetic trait prediction problems and showed that in general MINT is a better feature selection method than the state-of-the-art inductive method mRMR.

MINT: Mutual Information based Transductive Feature Selection for Genetic Trait Prediction

http://arxiv.org/pdf/1310.1659v1

Title MINT Mutual Information based Transductive Feature Selection Genetic Trait Prediction Summary Whole genome prediction complex phenotypic trait using highdensity genotyping array attracted great deal attention relevant field plant animal breeding genetic epidemiology number genotype generally much bigger number sample predictive model suffer curseofdimensionality curseofdimensionality problem affect computational efficiency particular genomic selection method also lead poor performance mainly due correlation among marker work proposed first transductive feature selection method based MRMR MaxRelevance MinRedundancy criterion call MINT applied MINT genetic trait prediction problem showed general MINT better feature selection method stateoftheart inductive method mRMR Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kalpesh Adhatrao', 'Aditya Gaykar', 'Amiraj Dhawan', 'Rohit Jha', 'Vipul Honrao']

1310.2071v1

An educational institution needs to have an approximate prior knowledge of enrolled students to predict their performance in future academics. This helps them to identify promising students and also provides them an opportunity to pay attention to and improve those who would probably get lower grades. As a solution, we have developed a system which can predict the performance of students from their previous performances using concepts of data mining techniques under Classification. We have analyzed the data set containing information about students, such as gender, marks scored in the board examinations of classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data, we have predicted the general and individual performance of freshly admitted students in future examinations.

Predicting Students' Performance Using ID3 And C4.5 Classification Algorithms

http://arxiv.org/pdf/1310.2071v1

Title Predicting Students Performance Using ID3 C45 Classification Algorithms Summary educational institution need approximate prior knowledge enrolled student predict performance future academic help identify promising student also provides opportunity pay attention improve would probably get lower grade solution developed system predict performance student previous performance using concept data mining technique Classification analyzed data set containing information student gender mark scored board examination class X XII mark rank entrance examination result first year previous batch student applying ID3 Iterative Dichotomiser 3 C45 classification algorithm data predicted general individual performance freshly admitted student future examination Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ofer Dekel', 'Jian Ding', 'Tomer Koren', 'Yuval Peres']

1310.2997v2

We study the adversarial multi-armed bandit problem in a setting where the player incurs a unit cost each time he switches actions. We prove that the player's $T$-round minimax regret in this setting is $\widetilde{\Theta}(T^{2/3})$, thereby closing a fundamental gap in our understanding of learning with bandit feedback. In the corresponding full-information version of the problem, the minimax regret is known to grow at a much slower rate of $\Theta(\sqrt{T})$. The difference between these two rates provides the \emph{first} indication that learning with bandit feedback can be significantly harder than learning with full-information feedback (previous results only showed a different dependence on the number of actions, but not on $T$.) In addition to characterizing the inherent difficulty of the multi-armed bandit problem with switching costs, our results also resolve several other open problems in online learning. One direct implication is that learning with bandit feedback against bounded-memory adaptive adversaries has a minimax regret of $\widetilde{\Theta}(T^{2/3})$. Another implication is that the minimax regret of online learning in adversarial Markov decision processes (MDPs) is $\widetilde{\Theta}(T^{2/3})$. The key to all of our results is a new randomized construction of a multi-scale random walk, which is of independent interest and likely to prove useful in additional settings.

Bandits with Switching Costs: T^{2/3} Regret

http://arxiv.org/pdf/1310.2997v2

Title Bandits Switching Costs T23 Regret Summary study adversarial multiarmed bandit problem setting player incurs unit cost time switch action prove player Tround minimax regret setting widetildeThetaT23 thereby closing fundamental gap understanding learning bandit feedback corresponding fullinformation version problem minimax regret known grow much slower rate ThetasqrtT difference two rate provides emphfirst indication learning bandit feedback significantly harder learning fullinformation feedback previous result showed different dependence number action addition characterizing inherent difficulty multiarmed bandit problem switching cost result also resolve several open problem online learning One direct implication learning bandit feedback boundedmemory adaptive adversary minimax regret widetildeThetaT23 Another implication minimax regret online learning adversarial Markov decision process MDPs widetildeThetaT23 key result new randomized construction multiscale random walk independent interest likely prove useful additional setting Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sameh Sorour', 'Yves Lostanlen', 'Shahrokh Valaee']

1310.3407v1

One major bottleneck in the practical implementation of received signal strength (RSS) based indoor localization systems is the extensive deployment efforts required to construct the radio maps through fingerprinting. In this paper, we aim to design an indoor localization scheme that can be directly employed without building a full fingerprinted radio map of the indoor environment. By accumulating the information of localized RSSs, this scheme can also simultaneously construct the radio map with limited calibration. To design this scheme, we employ a source data set that possesses the same spatial correlation of the RSSs in the indoor environment under study. The knowledge of this data set is then transferred to a limited number of calibration fingerprints and one or several RSS observations with unknown locations, in order to perform direct localization of these observations using manifold alignment. We test two different source data sets, namely a simulated radio propagation map and the environments plan coordinates. For moving users, we exploit the correlation of their observations to improve the localization accuracy. The online testing in two indoor environments shows that the plan coordinates achieve better results than the simulated radio maps, and a negligible degradation with 70-85% reduction in calibration load.

Joint Indoor Localization and Radio Map Construction with Limited Deployment Load

http://arxiv.org/pdf/1310.3407v1

Title Joint Indoor Localization Radio Map Construction Limited Deployment Load Summary One major bottleneck practical implementation received signal strength RSS based indoor localization system extensive deployment effort required construct radio map fingerprinting paper aim design indoor localization scheme directly employed without building full fingerprinted radio map indoor environment accumulating information localized RSSs scheme also simultaneously construct radio map limited calibration design scheme employ source data set posse spatial correlation RSSs indoor environment study knowledge data set transferred limited number calibration fingerprint one several RSS observation unknown location order perform direct localization observation using manifold alignment test two different source data set namely simulated radio propagation map environment plan coordinate moving user exploit correlation observation improve localization accuracy online testing two indoor environment show plan coordinate achieve better result simulated radio map negligible degradation 7085 reduction calibration load Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Adam Vaughan', 'Stanislav V. Bohac']

1310.3567v3

Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine combustion timing predictions must contend with non-linear chemistry, non-linear physics, period doubling bifurcation(s), turbulent mixing, model parameters that can drift day-to-day, and air-fuel mixture state information that cannot typically be resolved on a cycle-to-cycle basis, especially during transients. In previous work, an abstract cycle-to-cycle mapping function coupled with $\epsilon$-Support Vector Regression was shown to predict experimentally observed cycle-to-cycle combustion timing over a wide range of engine conditions, despite some of the aforementioned difficulties. The main limitation of the previous approach was that a partially acausual randomly sampled training dataset was used to train proof of concept offline predictions. The objective of this paper is to address this limitation by proposing a new online adaptive Extreme Learning Machine (ELM) extension named Weighted Ring-ELM. This extension enables fully causal combustion timing predictions at randomly chosen engine set points, and is shown to achieve results that are as good as or better than the previous offline method. The broader objective of this approach is to enable a new class of real-time model predictive control strategies for high variability HCCI and, ultimately, to bring HCCI's low engine-out NOx and reduced CO2 emissions to production engines.

An Extreme Learning Machine Approach to Predicting Near Chaotic HCCI Combustion Phasing in Real-Time

http://arxiv.org/pdf/1310.3567v3

Title Extreme Learning Machine Approach Predicting Near Chaotic HCCI Combustion Phasing RealTime Summary Fuel efficient Homogeneous Charge Compression Ignition HCCI engine combustion timing prediction must contend nonlinear chemistry nonlinear physic period doubling bifurcation turbulent mixing model parameter drift daytoday airfuel mixture state information cannot typically resolved cycletocycle basis especially transient previous work abstract cycletocycle mapping function coupled epsilonSupport Vector Regression shown predict experimentally observed cycletocycle combustion timing wide range engine condition despite aforementioned difficulty main limitation previous approach partially acausual randomly sampled training dataset used train proof concept offline prediction objective paper address limitation proposing new online adaptive Extreme Learning Machine ELM extension named Weighted RingELM extension enables fully causal combustion timing prediction randomly chosen engine set point shown achieve result good better previous offline method broader objective approach enable new class realtime model predictive control strategy high variability HCCI ultimately bring HCCIs low engineout NOx reduced CO2 emission production engine Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Albrecht Zimmermann', 'Sruthi Moorthy', 'Zifan Shi']

1310.3607v1

Most existing work on predicting NCAAB matches has been developed in a statistical context. Trusting the capabilities of ML techniques, particularly classification learners, to uncover the importance of features and learn their relationships, we evaluated a number of different paradigms on this task. In this paper, we summarize our work, pointing out that attributes seem to be more important than models, and that there seems to be an upper limit to predictive quality.

Predicting college basketball match outcomes using machine learning techniques: some results and lessons learned

http://arxiv.org/pdf/1310.3607v1

Title Predicting college basketball match outcome using machine learning technique result lesson learned Summary existing work predicting NCAAB match developed statistical context Trusting capability ML technique particularly classification learner uncover importance feature learn relationship evaluated number different paradigm task paper summarize work pointing attribute seem important model seems upper limit predictive quality Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hamidreza Chitsaz', 'Mohammad Aminisharifabad']

1310.4223v1

We consider the problem of exact learning of parameters of a linear RNA energy model from secondary structure data. A necessary and sufficient condition for learnability of parameters is derived, which is based on computing the convex hull of union of translated Newton polytopes of input sequences. The set of learned energy parameters is characterized as the convex cone generated by the normal vectors to those facets of the resulting polytope that are incident to the origin. In practice, the sufficient condition may not be satisfied by the entire training data set; hence, computing a maximal subset of training data for which the sufficient condition is satisfied is often desired. We show that problem is NP-hard in general for an arbitrary dimensional feature space. Using a randomized greedy algorithm, we select a subset of RNA STRAND v2.0 database that satisfies the sufficient condition for separate A-U, C-G, G-U base pair counting model. The set of learned energy parameters includes experimentally measured energies of A-U, C-G, and G-U pairs; hence, our parameter set is in agreement with the Turner parameters.

Exact Learning of RNA Energy Parameters From Structure

http://arxiv.org/pdf/1310.4223v1

Title Exact Learning RNA Energy Parameters Structure Summary consider problem exact learning parameter linear RNA energy model secondary structure data necessary sufficient condition learnability parameter derived based computing convex hull union translated Newton polytopes input sequence set learned energy parameter characterized convex cone generated normal vector facet resulting polytope incident origin practice sufficient condition may satisfied entire training data set hence computing maximal subset training data sufficient condition satisfied often desired show problem NPhard general arbitrary dimensional feature space Using randomized greedy algorithm select subset RNA STRAND v20 database satisfies sufficient condition separate AU CG GU base pair counting model set learned energy parameter includes experimentally measured energy AU CG GU pair hence parameter set agreement Turner parameter Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Francesco Orabona', 'Tamir Hazan', 'Anand D. Sarwate', 'Tommi Jaakkola']

1310.4227v1

The maximum a-posteriori (MAP) perturbation framework has emerged as a useful approach for inference and learning in high dimensional complex models. By maximizing a randomly perturbed potential function, MAP perturbations generate unbiased samples from the Gibbs distribution. Unfortunately, the computational cost of generating so many high-dimensional random variables can be prohibitive. More efficient algorithms use sequential sampling strategies based on the expected value of low dimensional MAP perturbations. This paper develops new measure concentration inequalities that bound the number of samples needed to estimate such expected values. Applying the general result to MAP perturbations can yield a more efficient algorithm to approximate sampling from the Gibbs distribution. The measure concentration result is of general interest and may be applicable to other areas involving expected estimations.

On Measure Concentration of Random Maximum A-Posteriori Perturbations

http://arxiv.org/pdf/1310.4227v1

Title Measure Concentration Random Maximum APosteriori Perturbations Summary maximum aposteriori MAP perturbation framework emerged useful approach inference learning high dimensional complex model maximizing randomly perturbed potential function MAP perturbation generate unbiased sample Gibbs distribution Unfortunately computational cost generating many highdimensional random variable prohibitive efficient algorithm use sequential sampling strategy based expected value low dimensional MAP perturbation paper develops new measure concentration inequality bound number sample needed estimate expected value Applying general result MAP perturbation yield efficient algorithm approximate sampling Gibbs distribution measure concentration result general interest may applicable area involving expected estimation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Juan Liu', 'Baochang Zhang', 'Linlin Shen', 'Jianzhuang Liu', 'Jason Zhao']

1310.4485v1

Keystroke Dynamics is an important biometric solution for person authentication. Based upon keystroke dynamics, this paper designs an embedded password protection device, develops an online system, collects two public databases for promoting the research on keystroke authentication, exploits the Gabor filter bank to characterize keystroke dynamics, and provides benchmark results of three popular classification algorithms, one-class support vector machine, Gaussian classifier, and nearest neighbour classifier.

The BeiHang Keystroke Dynamics Authentication System

http://arxiv.org/pdf/1310.4485v1

Title BeiHang Keystroke Dynamics Authentication System Summary Keystroke Dynamics important biometric solution person authentication Based upon keystroke dynamic paper design embedded password protection device develops online system collect two public database promoting research keystroke authentication exploit Gabor filter bank characterize keystroke dynamic provides benchmark result three popular classification algorithm oneclass support vector machine Gaussian classifier nearest neighbour classifier Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pokkuluri Kiran Sree', 'Inampudi Ramesh Babu', 'SSSN Usha Devi Nedunuri']

1310.4495v1

CA has grown as potential classifier for addressing major problems in bioinformatics. Lot of bioinformatics problems like predicting the protein coding region, finding the promoter region, predicting the structure of protein and many other problems in bioinformatics can be addressed through Cellular Automata. Even though there are some prediction techniques addressing these problems, the approximate accuracy level is very less. An automated procedure was proposed with MACA (Multiple Attractor Cellular Automata) which can address all these problems. The genetic algorithm is also used to find rules with good fitness values. Extensive experiments are conducted for reporting the accuracy of the proposed tool. The average accuracy of MACA when tested with ENCODE, BG570, HMR195, Fickett and Tongue, ASP67 datasets is 78%.

Multiple Attractor Cellular Automata (MACA) for Addressing Major Problems in Bioinformatics

http://arxiv.org/pdf/1310.4495v1

Title Multiple Attractor Cellular Automata MACA Addressing Major Problems Bioinformatics Summary CA grown potential classifier addressing major problem bioinformatics Lot bioinformatics problem like predicting protein coding region finding promoter region predicting structure protein many problem bioinformatics addressed Cellular Automata Even though prediction technique addressing problem approximate accuracy level le automated procedure proposed MACA Multiple Attractor Cellular Automata address problem genetic algorithm also used find rule good fitness value Extensive experiment conducted reporting accuracy proposed tool average accuracy MACA tested ENCODE BG570 HMR195 Fickett Tongue ASP67 datasets 78 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['G. Srinivas Rao', 'A. V. Ramana']

1310.8467v1

Routing packets opportunistically is an essential part of multihop ad hoc wireless sensor networks. The existing routing techniques are not adaptive opportunistic. In this paper we have proposed an adaptive opportunistic routing scheme that routes packets opportunistically in order to ensure that packet loss is avoided. Learning and routing are combined in the framework that explores the optimal routing possibilities. In this paper we implemented this Reinforced learning framework using a customer simulator. The experimental results revealed that the scheme is able to exploit the opportunistic to optimize routing of packets even though the network structure is unknown.

Reinforcement Learning Framework for Opportunistic Routing in WSNs

http://arxiv.org/pdf/1310.8467v1

Title Reinforcement Learning Framework Opportunistic Routing WSNs Summary Routing packet opportunistically essential part multihop ad hoc wireless sensor network existing routing technique adaptive opportunistic paper proposed adaptive opportunistic routing scheme route packet opportunistically order ensure packet loss avoided Learning routing combined framework explores optimal routing possibility paper implemented Reinforced learning framework using customer simulator experimental result revealed scheme able exploit opportunistic optimize routing packet even though network structure unknown Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dhruv Mahajan', 'S. Sathiya Keerthi', 'S. Sundararajan', 'Leon Bottou']

1311.0636v1

This paper proposes a novel parallel stochastic gradient descent (SGD) method that is obtained by applying parallel sets of SGD iterations (each set operating on one node using the data residing in it) for finding the direction in each iteration of a batch descent method. The method has strong convergence properties. Experiments on datasets with high dimensional feature spaces show the value of this method.

A Parallel SGD method with Strong Convergence

http://arxiv.org/pdf/1311.0636v1

Title Parallel SGD method Strong Convergence Summary paper proposes novel parallel stochastic gradient descent SGD method obtained applying parallel set SGD iteration set operating one node using data residing finding direction iteration batch descent method method strong convergence property Experiments datasets high dimensional feature space show value method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Alexander Rakhlin', 'Karthik Sridharan']

1311.1869v1

We provide several applications of Optimistic Mirror Descent, an online learning algorithm based on the idea of predictable sequences. First, we recover the Mirror Prox algorithm for offline optimization, prove an extension to Holder-smooth functions, and apply the results to saddle-point type problems. Next, we prove that a version of Optimistic Mirror Descent (which has a close relation to the Exponential Weights algorithm) can be used by two strongly-uncoupled players in a finite zero-sum matrix game to converge to the minimax equilibrium at the rate of O((log T)/T). This addresses a question of Daskalakis et al 2011. Further, we consider a partial information version of the problem. We then apply the results to convex programming and exhibit a simple algorithm for the approximate Max Flow problem.

Optimization, Learning, and Games with Predictable Sequences

http://arxiv.org/pdf/1311.1869v1

Title Optimization Learning Games Predictable Sequences Summary provide several application Optimistic Mirror Descent online learning algorithm based idea predictable sequence First recover Mirror Prox algorithm offline optimization prove extension Holdersmooth function apply result saddlepoint type problem Next prove version Optimistic Mirror Descent close relation Exponential Weights algorithm used two stronglyuncoupled player finite zerosum matrix game converge minimax equilibrium rate Olog TT address question Daskalakis et al 2011 consider partial information version problem apply result convex programming exhibit simple algorithm approximate Max Flow problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Amit Daniely', 'Nati Linial', 'Shai Shalev-Shwartz']

1311.2272v2

The basic problem in the PAC model of computational learning theory is to determine which hypothesis classes are efficiently learnable. There is presently a dearth of results showing hardness of learning problems. Moreover, the existing lower bounds fall short of the best known algorithms. The biggest challenge in proving complexity results is to establish hardness of {\em improper learning} (a.k.a. representation independent learning).The difficulty in proving lower bounds for improper learning is that the standard reductions from $\mathbf{NP}$-hard problems do not seem to apply in this context. There is essentially only one known approach to proving lower bounds on improper learning. It was initiated in (Kearns and Valiant 89) and relies on cryptographic assumptions. We introduce a new technique for proving hardness of improper learning, based on reductions from problems that are hard on average. We put forward a (fairly strong) generalization of Feige's assumption (Feige 02) about the complexity of refuting random constraint satisfaction problems. Combining this assumption with our new technique yields far reaching implications. In particular, 1. Learning $\mathrm{DNF}$'s is hard. 2. Agnostically learning halfspaces with a constant approximation ratio is hard. 3. Learning an intersection of $\omega(1)$ halfspaces is hard.

From average case complexity to improper learning complexity

http://arxiv.org/pdf/1311.2272v2

Title average case complexity improper learning complexity Summary basic problem PAC model computational learning theory determine hypothesis class efficiently learnable presently dearth result showing hardness learning problem Moreover existing lower bound fall short best known algorithm biggest challenge proving complexity result establish hardness em improper learning aka representation independent learningThe difficulty proving lower bound improper learning standard reduction mathbfNPhard problem seem apply context essentially one known approach proving lower bound improper learning initiated Kearns Valiant 89 relies cryptographic assumption introduce new technique proving hardness improper learning based reduction problem hard average put forward fairly strong generalization Feiges assumption Feige 02 complexity refuting random constraint satisfaction problem Combining assumption new technique yield far reaching implication particular 1 Learning mathrmDNFs hard 2 Agnostically learning halfspaces constant approximation ratio hard 3 Learning intersection omega1 halfspaces hard Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Moritz Hardt', 'Eric Price']

1311.2495v4

We provide a new robust convergence analysis of the well-known power method for computing the dominant singular vectors of a matrix that we call the noisy power method. Our result characterizes the convergence behavior of the algorithm when a significant amount noise is introduced after each matrix-vector multiplication. The noisy power method can be seen as a meta-algorithm that has recently found a number of important applications in a broad range of machine learning problems including alternating minimization for matrix completion, streaming principal component analysis (PCA), and privacy-preserving spectral analysis. Our general analysis subsumes several existing ad-hoc convergence bounds and resolves a number of open problems in multiple applications including streaming PCA and privacy-preserving singular vector computation.

The Noisy Power Method: A Meta Algorithm with Applications

http://arxiv.org/pdf/1311.2495v4

Title Noisy Power Method Meta Algorithm Applications Summary provide new robust convergence analysis wellknown power method computing dominant singular vector matrix call noisy power method result characterizes convergence behavior algorithm significant amount noise introduced matrixvector multiplication noisy power method seen metaalgorithm recently found number important application broad range machine learning problem including alternating minimization matrix completion streaming principal component analysis PCA privacypreserving spectral analysis general analysis subsumes several existing adhoc convergence bound resolve number open problem multiple application including streaming PCA privacypreserving singular vector computation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Christos Boutsidis', 'Alex Gittens', 'Prabhanjan Kambadur']

1311.2854v3

Spectral clustering is one of the most important algorithms in data mining and machine intelligence; however, its computational complexity limits its application to truly large scale data analysis. The computational bottleneck in spectral clustering is computing a few of the top eigenvectors of the (normalized) Laplacian matrix corresponding to the graph representing the data to be clustered. One way to speed up the computation of these eigenvectors is to use the "power method" from the numerical linear algebra literature. Although the power method has been empirically used to speed up spectral clustering, the theory behind this approach, to the best of our knowledge, remains unexplored. This paper provides the \emph{first} such rigorous theoretical justification, arguing that a small number of power iterations suffices to obtain near-optimal partitionings using the approximate eigenvectors. Specifically, we prove that solving the $k$-means clustering problem on the approximate eigenvectors obtained via the power method gives an additive-error approximation to solving the $k$-means problem on the optimal eigenvectors.

Spectral Clustering via the Power Method -- Provably

http://arxiv.org/pdf/1311.2854v3

Title Spectral Clustering via Power Method Provably Summary Spectral clustering one important algorithm data mining machine intelligence however computational complexity limit application truly large scale data analysis computational bottleneck spectral clustering computing top eigenvectors normalized Laplacian matrix corresponding graph representing data clustered One way speed computation eigenvectors use power method numerical linear algebra literature Although power method empirically used speed spectral clustering theory behind approach best knowledge remains unexplored paper provides emphfirst rigorous theoretical justification arguing small number power iteration suffices obtain nearoptimal partitioning using approximate eigenvectors Specifically prove solving kmeans clustering problem approximate eigenvectors obtained via power method give additiveerror approximation solving kmeans problem optimal eigenvectors Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Nan Du', 'Le Song', 'Manuel Gomez Rodriguez', 'Hongyuan Zha']

1311.3669v1

If a piece of information is released from a media site, can it spread, in 1 month, to a million web pages? This influence estimation problem is very challenging since both the time-sensitive nature of the problem and the issue of scalability need to be addressed simultaneously. In this paper, we propose a randomized algorithm for influence estimation in continuous-time diffusion networks. Our algorithm can estimate the influence of every node in a network with |V| nodes and |E| edges to an accuracy of $\varepsilon$ using $n=O(1/\varepsilon^2)$ randomizations and up to logarithmic factors O(n|E|+n|V|) computations. When used as a subroutine in a greedy influence maximization algorithm, our proposed method is guaranteed to find a set of nodes with an influence of at least (1-1/e)OPT-2$\varepsilon$, where OPT is the optimal value. Experiments on both synthetic and real-world data show that the proposed method can easily scale up to networks of millions of nodes while significantly improves over previous state-of-the-arts in terms of the accuracy of the estimated influence and the quality of the selected nodes in maximizing the influence.

Scalable Influence Estimation in Continuous-Time Diffusion Networks

http://arxiv.org/pdf/1311.3669v1

Title Scalable Influence Estimation ContinuousTime Diffusion Networks Summary piece information released medium site spread 1 month million web page influence estimation problem challenging since timesensitive nature problem issue scalability need addressed simultaneously paper propose randomized algorithm influence estimation continuoustime diffusion network algorithm estimate influence every node network V node E edge accuracy varepsilon using nO1varepsilon2 randomization logarithmic factor OnEnV computation used subroutine greedy influence maximization algorithm proposed method guaranteed find set node influence least 11eOPT2varepsilon OPT optimal value Experiments synthetic realworld data show proposed method easily scale network million node significantly improves previous stateofthearts term accuracy estimated influence quality selected node maximizing influence Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shaona Ghosh', 'Adam Prugel-Bennett']

1311.5022v3

On-line linear optimization on combinatorial action sets (d-dimensional actions) with bandit feedback, is known to have complexity in the order of the dimension of the problem. The exponential weighted strategy achieves the best known regret bound that is of the order of $d^{2}\sqrt{n}$ (where $d$ is the dimension of the problem, $n$ is the time horizon). However, such strategies are provably suboptimal or computationally inefficient. The complexity is attributed to the combinatorial structure of the action set and the dearth of efficient exploration strategies of the set. Mirror descent with entropic regularization function comes close to solving this problem by enforcing a meticulous projection of weights with an inherent boundary condition. Entropic regularization in mirror descent is the only known way of achieving a logarithmic dependence on the dimension. Here, we argue otherwise and recover the original intuition of exponential weighting by borrowing a technique from discrete optimization and approximation algorithms called `extended formulation'. Such formulations appeal to the underlying geometry of the set with a guaranteed logarithmic dependence on the dimension underpinned by an information theoretic entropic analysis.

Extended Formulations for Online Linear Bandit Optimization

http://arxiv.org/pdf/1311.5022v3

Title Extended Formulations Online Linear Bandit Optimization Summary Online linear optimization combinatorial action set ddimensional action bandit feedback known complexity order dimension problem exponential weighted strategy achieves best known regret bound order d2sqrtn dimension problem n time horizon However strategy provably suboptimal computationally inefficient complexity attributed combinatorial structure action set dearth efficient exploration strategy set Mirror descent entropic regularization function come close solving problem enforcing meticulous projection weight inherent boundary condition Entropic regularization mirror descent known way achieving logarithmic dependence dimension argue otherwise recover original intuition exponential weighting borrowing technique discrete optimization approximation algorithm called extended formulation formulation appeal underlying geometry set guaranteed logarithmic dependence dimension underpinned information theoretic entropic analysis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Smriti Bhagat', 'Udi Weinsberg', 'Stratis Ioannidis', 'Nina Taft']

1311.6802v2

Recommender systems leverage user demographic information, such as age, gender, etc., to personalize recommendations and better place their targeted ads. Oftentimes, users do not volunteer this information due to privacy concerns, or due to a lack of initiative in filling out their online profiles. We illustrate a new threat in which a recommender learns private attributes of users who do not voluntarily disclose them. We design both passive and active attacks that solicit ratings for strategically selected items, and could thus be used by a recommender system to pursue this hidden agenda. Our methods are based on a novel usage of Bayesian matrix factorization in an active learning setting. Evaluations on multiple datasets illustrate that such attacks are indeed feasible and use significantly fewer rated items than static inference methods. Importantly, they succeed without sacrificing the quality of recommendations to users.

Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization

http://arxiv.org/pdf/1311.6802v2

Title Recommending Agenda Active Learning Private Attributes using Matrix Factorization Summary Recommender system leverage user demographic information age gender etc personalize recommendation better place targeted ad Oftentimes user volunteer information due privacy concern due lack initiative filling online profile illustrate new threat recommender learns private attribute user voluntarily disclose design passive active attack solicit rating strategically selected item could thus used recommender system pursue hidden agenda method based novel usage Bayesian matrix factorization active learning setting Evaluations multiple datasets illustrate attack indeed feasible use significantly fewer rated item static inference method Importantly succeed without sacrificing quality recommendation user Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kareem Amin', 'Afshin Rostamizadeh', 'Umar Syed']

1311.6838v1

Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer's value distribution for a good when the buyer is repeatedly interacting with a seller through a posted-price mechanism. We model the buyer as a strategic agent, whose goal is to maximize her long-term surplus, and we are interested in mechanisms that maximize the seller's long-term revenue. We define the natural notion of strategic regret --- the lost revenue as measured against a truthful (non-strategic) buyer. We present seller algorithms that are no-(strategic)-regret when the buyer discounts her future surplus --- i.e. the buyer prefers showing advertisements to users sooner rather than later. We also give a lower bound on strategic regret that increases as the buyer's discounting weakens and shows, in particular, that any seller algorithm will suffer linear strategic regret if there is no discounting.

Learning Prices for Repeated Auctions with Strategic Buyers

http://arxiv.org/pdf/1311.6838v1

Title Learning Prices Repeated Auctions Strategic Buyers Summary Inspired realtime ad exchange online display advertising consider problem inferring buyer value distribution good buyer repeatedly interacting seller postedprice mechanism model buyer strategic agent whose goal maximize longterm surplus interested mechanism maximize seller longterm revenue define natural notion strategic regret lost revenue measured truthful nonstrategic buyer present seller algorithm nostrategicregret buyer discount future surplus ie buyer prefers showing advertisement user sooner rather later also give lower bound strategic regret increase buyer discounting weakens show particular seller algorithm suffer linear strategic regret discounting Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tianbao Yang', 'Shenghuo Zhu', 'Rong Jin', 'Yuanqing Lin']

1312.1031v2

In \citep{Yangnips13}, the author presented distributed stochastic dual coordinate ascent (DisDCA) algorithms for solving large-scale regularized loss minimization. Extraordinary performances have been observed and reported for the well-motivated updates, as referred to the practical updates, compared to the naive updates. However, no serious analysis has been provided to understand the updates and therefore the convergence rates. In the paper, we bridge the gap by providing a theoretical analysis of the convergence rates of the practical DisDCA algorithm. Our analysis helped by empirical studies has shown that it could yield an exponential speed-up in the convergence by increasing the number of dual updates at each iteration. This result justifies the superior performances of the practical DisDCA as compared to the naive variant. As a byproduct, our analysis also reveals the convergence behavior of the one-communication DisDCA.

Analysis of Distributed Stochastic Dual Coordinate Ascent

http://arxiv.org/pdf/1312.1031v2

Title Analysis Distributed Stochastic Dual Coordinate Ascent Summary citepYangnips13 author presented distributed stochastic dual coordinate ascent DisDCA algorithm solving largescale regularized loss minimization Extraordinary performance observed reported wellmotivated update referred practical update compared naive update However serious analysis provided understand update therefore convergence rate paper bridge gap providing theoretical analysis convergence rate practical DisDCA algorithm analysis helped empirical study shown could yield exponential speedup convergence increasing number dual update iteration result justifies superior performance practical DisDCA compared naive variant byproduct analysis also reveals convergence behavior onecommunication DisDCA Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Robert Kleinberg', 'Aleksandrs Slivkins', 'Eli Upfal']

1312.1277v2

In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with large strategy sets are still a topic of very active investigation, motivated by practical applications such as online auctions and web advertisement. The goal of such research is to identify broad and natural classes of strategy sets and payoff functions which enable the design of efficient solutions. In this work we study a very general setting for the multi-armed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric. We refer to this problem as the "Lipschitz MAB problem". We present a solution for the multi-armed bandit problem in this setting. That is, for every metric space we define an isometry invariant which bounds from below the performance of Lipschitz MAB algorithms for this metric space, and we present an algorithm which comes arbitrarily close to meeting this bound. Furthermore, our technique gives even better results for benign payoff functions. We also address the full-feedback ("best expert") version of the problem, where after every round the payoffs from all arms are revealed.

Bandits and Experts in Metric Spaces

http://arxiv.org/pdf/1312.1277v2

Title Bandits Experts Metric Spaces Summary multiarmed bandit problem online algorithm chooses set strategy sequence trial maximize total payoff chosen strategy performance bandit algorithm small finite strategy set quite well understood bandit problem large strategy set still topic active investigation motivated practical application online auction web advertisement goal research identify broad natural class strategy set payoff function enable design efficient solution work study general setting multiarmed bandit problem strategy form metric space payoff function satisfies Lipschitz condition respect metric refer problem Lipschitz MAB problem present solution multiarmed bandit problem setting every metric space define isometry invariant bound performance Lipschitz MAB algorithm metric space present algorithm come arbitrarily close meeting bound Furthermore technique give even better result benign payoff function also address fullfeedback best expert version problem every round payoff arm revealed Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ferhat Özgür Çatak', 'Mehmet Erdal Balaban']

1312.4108v1

Although Support Vector Machine (SVM) algorithm has a high generalization property to classify for unseen examples after training phase and it has small loss value, the algorithm is not suitable for real-life classification and regression problems. SVMs cannot solve hundreds of thousands examples in training dataset. In previous studies on distributed machine learning algorithms, SVM is trained over a costly and preconfigured computer environment. In this research, we present a MapReduce based distributed parallel SVM training algorithm for binary classification problems. This work shows how to distribute optimization problem over cloud computing systems with MapReduce technique. In the second step of this work, we used statistical learning theory to find the predictive hypothesis that minimize our empirical risks from hypothesis spaces that created with reduce function of MapReduce. The results of this research are important for training of big datasets for SVM algorithm based classification problems. We provided that iterative training of split dataset with MapReduce technique; accuracy of the classifier function will converge to global optimal classifier function's accuracy in finite iteration size. The algorithm performance was measured on samples from letter recognition and pen-based recognition of handwritten digits dataset.

A MapReduce based distributed SVM algorithm for binary classification

http://arxiv.org/pdf/1312.4108v1

Title MapReduce based distributed SVM algorithm binary classification Summary Although Support Vector Machine SVM algorithm high generalization property classify unseen example training phase small loss value algorithm suitable reallife classification regression problem SVMs cannot solve hundred thousand example training dataset previous study distributed machine learning algorithm SVM trained costly preconfigured computer environment research present MapReduce based distributed parallel SVM training algorithm binary classification problem work show distribute optimization problem cloud computing system MapReduce technique second step work used statistical learning theory find predictive hypothesis minimize empirical risk hypothesis space created reduce function MapReduce result research important training big datasets SVM algorithm based classification problem provided iterative training split dataset MapReduce technique accuracy classifier function converge global optimal classifier function accuracy finite iteration size algorithm performance measured sample letter recognition penbased recognition handwritten digit dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Gabriele Oliva', 'Roberto Setola', 'Christoforos N. Hadjicostis']

1312.4176v3

In this paper we provide a fully distributed implementation of the k-means clustering algorithm, intended for wireless sensor networks where each agent is endowed with a possibly high-dimensional observation (e.g., position, humidity, temperature, etc.) The proposed algorithm, by means of one-hop communication, partitions the agents into measure-dependent groups that have small in-group and large out-group "distances". Since the partitions may not have a relation with the topology of the network--members of the same clusters may not be spatially close--the algorithm is provided with a mechanism to compute the clusters'centroids even when the clusters are disconnected in several sub-clusters.The results of the proposed distributed algorithm coincide, in terms of minimization of the objective function, with the centralized k-means algorithm. Some numerical examples illustrate the capabilities of the proposed solution.

Distributed k-means algorithm

http://arxiv.org/pdf/1312.4176v3

Title Distributed kmeans algorithm Summary paper provide fully distributed implementation kmeans clustering algorithm intended wireless sensor network agent endowed possibly highdimensional observation eg position humidity temperature etc proposed algorithm mean onehop communication partition agent measuredependent group small ingroup large outgroup distance Since partition may relation topology networkmembers cluster may spatially closethe algorithm provided mechanism compute clusterscentroids even cluster disconnected several subclustersThe result proposed distributed algorithm coincide term minimization objective function centralized kmeans algorithm numerical example illustrate capability proposed solution Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yaqub Alwan', 'Zoran Cvetkovic', 'Michael Curtis']

1312.5354v2

We studied classification of human ECGs labelled as normal sinus rhythm, ventricular fibrillation and ventricular tachycardia by means of support vector machines in different representation spaces, using different observation lengths. ECG waveform segments of duration 0.5-4 s, their Fourier magnitude spectra, and lower dimensional projections of Fourier magnitude spectra were used for classification. All considered representations were of much higher dimension than in published studies. Classification accuracy improved with segment duration up to 2 s, with 4 s providing little improvement. We found that it is possible to discriminate between ventricular tachycardia and ventricular fibrillation by the present approach with much shorter runs of ECG (2 s, minimum 86% sensitivity per class) than previously imagined. Ensembles of classifiers acting on 1 s segments taken over 5 s observation windows gave best results, with sensitivities of detection for all classes exceeding 93%.

Classification of Human Ventricular Arrhythmia in High Dimensional Representation Spaces

http://arxiv.org/pdf/1312.5354v2

Title Classification Human Ventricular Arrhythmia High Dimensional Representation Spaces Summary studied classification human ECGs labelled normal sinus rhythm ventricular fibrillation ventricular tachycardia mean support vector machine different representation space using different observation length ECG waveform segment duration 054 Fourier magnitude spectrum lower dimensional projection Fourier magnitude spectrum used classification considered representation much higher dimension published study Classification accuracy improved segment duration 2 4 providing little improvement found possible discriminate ventricular tachycardia ventricular fibrillation present approach much shorter run ECG 2 minimum 86 sensitivity per class previously imagined Ensembles classifier acting 1 segment taken 5 observation window gave best result sensitivity detection class exceeding 93 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['W. Liu', 'H. Liu', 'D. Tao', 'Y. Wang', 'K. Lu']

1312.6180v1

With the rapid advance of Internet technology and smart devices, users often need to manage large amounts of multimedia information using smart devices, such as personal image and video accessing and browsing. These requirements heavily rely on the success of image (video) annotation, and thus large scale image annotation through innovative machine learning methods has attracted intensive attention in recent years. One representative work is support vector machine (SVM). Although it works well in binary classification, SVM has a non-smooth loss function and can not naturally cover multi-class case. In this paper, we propose manifold regularized kernel logistic regression (KLR) for web image annotation. Compared to SVM, KLR has the following advantages: (1) the KLR has a smooth loss function; (2) the KLR produces an explicit estimate of the probability instead of class label; and (3) the KLR can naturally be generalized to the multi-class case. We carefully conduct experiments on MIR FLICKR dataset and demonstrate the effectiveness of manifold regularized kernel logistic regression for image annotation.

Manifold regularized kernel logistic regression for web image annotation

http://arxiv.org/pdf/1312.6180v1

Title Manifold regularized kernel logistic regression web image annotation Summary rapid advance Internet technology smart device user often need manage large amount multimedia information using smart device personal image video accessing browsing requirement heavily rely success image video annotation thus large scale image annotation innovative machine learning method attracted intensive attention recent year One representative work support vector machine SVM Although work well binary classification SVM nonsmooth loss function naturally cover multiclass case paper propose manifold regularized kernel logistic regression KLR web image annotation Compared SVM KLR following advantage 1 KLR smooth loss function 2 KLR produce explicit estimate probability instead class label 3 KLR naturally generalized multiclass case carefully conduct experiment MIR FLICKR dataset demonstrate effectiveness manifold regularized kernel logistic regression image annotation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Luis Marujo', 'Anatole Gershman', 'Jaime Carbonell', 'David Martins de Matos', 'João P. Neto']

1312.6597v2

In this work, we propose two stochastic architectural models (CMC and CMC-M) with two layers of classifiers applicable to datasets with one and multiple skewed classes. This distinction becomes important when the datasets have a large number of classes. Therefore, we present a novel solution to imbalanced multiclass learning with several skewed majority classes, which improves minority classes identification. This fact is particularly important for text classification tasks, such as event detection. Our models combined with pre-processing sampling techniques improved the classification results on six well-known datasets. Finally, we have also introduced a new metric SG-Mean to overcome the multiplication by zero limitation of G-Mean.

Co-Multistage of Multiple Classifiers for Imbalanced Multiclass Learning

http://arxiv.org/pdf/1312.6597v2

Title CoMultistage Multiple Classifiers Imbalanced Multiclass Learning Summary work propose two stochastic architectural model CMC CMCM two layer classifier applicable datasets one multiple skewed class distinction becomes important datasets large number class Therefore present novel solution imbalanced multiclass learning several skewed majority class improves minority class identification fact particularly important text classification task event detection model combined preprocessing sampling technique improved classification result six wellknown datasets Finally also introduced new metric SGMean overcome multiplication zero limitation GMean Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pranjal Awasthi', 'Maria-Florina Balcan', 'Konstantin Voevodski']

1312.6724v3

We study the design of interactive clustering algorithms for data sets satisfying natural stability assumptions. Our algorithms start with any initial clustering and only make local changes in each step; both are desirable features in many applications. We show that in this constrained setting one can still design provably efficient algorithms that produce accurate clusterings. We also show that our algorithms perform well on real-world data.

Local algorithms for interactive clustering

http://arxiv.org/pdf/1312.6724v3

Title Local algorithm interactive clustering Summary study design interactive clustering algorithm data set satisfying natural stability assumption algorithm start initial clustering make local change step desirable feature many application show constrained setting one still design provably efficient algorithm produce accurate clustering also show algorithm perform well realworld data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ahmed K. Farahat', 'Ahmed Elgohary', 'Ali Ghodsi', 'Mohamed S. Kamel']

1312.6838v1

In today's information systems, the availability of massive amounts of data necessitates the development of fast and accurate algorithms to summarize these data and represent them in a succinct format. One crucial problem in big data analytics is the selection of representative instances from large and massively-distributed data, which is formally known as the Column Subset Selection (CSS) problem. The solution to this problem enables data analysts to understand the insights of the data and explore its hidden structure. The selected instances can also be used for data preprocessing tasks such as learning a low-dimensional embedding of the data points or computing a low-rank approximation of the corresponding matrix. This paper presents a fast and accurate greedy algorithm for large-scale column subset selection. The algorithm minimizes an objective function which measures the reconstruction error of the data matrix based on the subset of selected columns. The paper first presents a centralized greedy algorithm for column subset selection which depends on a novel recursive formula for calculating the reconstruction error of the data matrix. The paper then presents a MapReduce algorithm which selects a few representative columns from a matrix whose columns are massively distributed across several commodity machines. The algorithm first learns a concise representation of all columns using random projection, and it then solves a generalized column subset selection problem at each machine in which a subset of columns are selected from the sub-matrix on that machine such that the reconstruction error of the concise representation is minimized. The paper demonstrates the effectiveness and efficiency of the proposed algorithm through an empirical evaluation on benchmark data sets.

Greedy Column Subset Selection for Large-scale Data Sets

http://arxiv.org/pdf/1312.6838v1

Title Greedy Column Subset Selection Largescale Data Sets Summary today information system availability massive amount data necessitates development fast accurate algorithm summarize data represent succinct format One crucial problem big data analytics selection representative instance large massivelydistributed data formally known Column Subset Selection CSS problem solution problem enables data analyst understand insight data explore hidden structure selected instance also used data preprocessing task learning lowdimensional embedding data point computing lowrank approximation corresponding matrix paper present fast accurate greedy algorithm largescale column subset selection algorithm minimizes objective function measure reconstruction error data matrix based subset selected column paper first present centralized greedy algorithm column subset selection depends novel recursive formula calculating reconstruction error data matrix paper present MapReduce algorithm selects representative column matrix whose column massively distributed across several commodity machine algorithm first learns concise representation column using random projection solves generalized column subset selection problem machine subset column selected submatrix machine reconstruction error concise representation minimized paper demonstrates effectiveness efficiency proposed algorithm empirical evaluation benchmark data set Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Anupriya Gogna', 'Ankita Shukla', 'Angshul Majumdar']

1312.6872v1

In this paper we address the problem of recovering a matrix, with inherent low rank structure, from its lower dimensional projections. This problem is frequently encountered in wide range of areas including pattern recognition, wireless sensor networks, control systems, recommender systems, image/video reconstruction etc. Both in theory and practice, the most optimal way to solve the low rank matrix recovery problem is via nuclear norm minimization. In this paper, we propose a Split Bregman algorithm for nuclear norm minimization. The use of Bregman technique improves the convergence speed of our algorithm and gives a higher success rate. Also, the accuracy of reconstruction is much better even for cases where small number of linear measurements are available. Our claim is supported by empirical results obtained using our algorithm and its comparison to other existing methods for matrix recovery. The algorithms are compared on the basis of NMSE, execution time and success rate for varying ranks and sampling ratios.

Matrix recovery using Split Bregman

http://arxiv.org/pdf/1312.6872v1

Title Matrix recovery using Split Bregman Summary paper address problem recovering matrix inherent low rank structure lower dimensional projection problem frequently encountered wide range area including pattern recognition wireless sensor network control system recommender system imagevideo reconstruction etc theory practice optimal way solve low rank matrix recovery problem via nuclear norm minimization paper propose Split Bregman algorithm nuclear norm minimization use Bregman technique improves convergence speed algorithm give higher success rate Also accuracy reconstruction much better even case small number linear measurement available claim supported empirical result obtained using algorithm comparison existing method matrix recovery algorithm compared basis NMSE execution time success rate varying rank sampling ratio Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Prashanth L. A.', 'Abhranil Chatterjee', 'Shalabh Bhatnagar']

1312.7292v2

In this paper, we consider an intrusion detection application for Wireless Sensor Networks (WSNs). We study the problem of scheduling the sleep times of the individual sensors to maximize the network lifetime while keeping the tracking error to a minimum. We formulate this problem as a partially-observable Markov decision process (POMDP) with continuous state-action spaces, in a manner similar to (Fuemmeler and Veeravalli [2008]). However, unlike their formulation, we consider infinite horizon discounted and average cost objectives as performance criteria. For each criterion, we propose a convergent on-policy Q-learning algorithm that operates on two timescales, while employing function approximation to handle the curse of dimensionality associated with the underlying POMDP. Our proposed algorithm incorporates a policy gradient update using a one-simulation simultaneous perturbation stochastic approximation (SPSA) estimate on the faster timescale, while the Q-value parameter (arising from a linear function approximation for the Q-values) is updated in an on-policy temporal difference (TD) algorithm-like fashion on the slower timescale. The feature selection scheme employed in each of our algorithms manages the energy and tracking components in a manner that assists the search for the optimal sleep-scheduling policy. For the sake of comparison, in both discounted and average settings, we also develop a function approximation analogue of the Q-learning algorithm. This algorithm, unlike the two-timescale variant, does not possess theoretical convergence guarantees. Finally, we also adapt our algorithms to include a stochastic iterative estimation scheme for the intruder's mobility model. Our simulation results on a 2-dimensional network setting suggest that our algorithms result in better tracking accuracy at the cost of only a few additional sensors, in comparison to a recent prior work.

Two Timescale Convergent Q-learning for Sleep--Scheduling in Wireless Sensor Networks

http://arxiv.org/pdf/1312.7292v2

Title Two Timescale Convergent Qlearning SleepScheduling Wireless Sensor Networks Summary paper consider intrusion detection application Wireless Sensor Networks WSNs study problem scheduling sleep time individual sensor maximize network lifetime keeping tracking error minimum formulate problem partiallyobservable Markov decision process POMDP continuous stateaction space manner similar Fuemmeler Veeravalli 2008 However unlike formulation consider infinite horizon discounted average cost objective performance criterion criterion propose convergent onpolicy Qlearning algorithm operates two timescales employing function approximation handle curse dimensionality associated underlying POMDP proposed algorithm incorporates policy gradient update using onesimulation simultaneous perturbation stochastic approximation SPSA estimate faster timescale Qvalue parameter arising linear function approximation Qvalues updated onpolicy temporal difference TD algorithmlike fashion slower timescale feature selection scheme employed algorithm manages energy tracking component manner assist search optimal sleepscheduling policy sake comparison discounted average setting also develop function approximation analogue Qlearning algorithm algorithm unlike twotimescale variant posse theoretical convergence guarantee Finally also adapt algorithm include stochastic iterative estimation scheme intruder mobility model simulation result 2dimensional network setting suggest algorithm result better tracking accuracy cost additional sensor comparison recent prior work Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Christopher Genovese', 'Marco Perone-Pacifico', 'Isabella Verdinelli', 'Larry Wasserman']

1312.7567v1

We derive nonparametric confidence intervals for the eigenvalues of the Hessian at modes of a density estimate. This provides information about the strength and shape of modes and can also be used as a significance test. We use a data-splitting approach in which potential modes are identified using the first half of the data and inference is done with the second half of the data. To get valid confidence sets for the eigenvalues, we use a bootstrap based on an elementary-symmetric-polynomial (ESP) transformation. This leads to valid bootstrap confidence sets regardless of any multiplicities in the eigenvalues. We also suggest a new method for bandwidth selection, namely, choosing the bandwidth to maximize the number of significant modes. We show by example that this method works well. Even when the true distribution is singular, and hence does not have a density, (in which case cross validation chooses a zero bandwidth), our method chooses a reasonable bandwidth.

Nonparametric Inference For Density Modes

http://arxiv.org/pdf/1312.7567v1

Title Nonparametric Inference Density Modes Summary derive nonparametric confidence interval eigenvalue Hessian mode density estimate provides information strength shape mode also used significance test use datasplitting approach potential mode identified using first half data inference done second half data get valid confidence set eigenvalue use bootstrap based elementarysymmetricpolynomial ESP transformation lead valid bootstrap confidence set regardless multiplicity eigenvalue also suggest new method bandwidth selection namely choosing bandwidth maximize number significant mode show example method work well Even true distribution singular hence density case cross validation chooses zero bandwidth method chooses reasonable bandwidth Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Andrey Bernstein', 'Nahum Shimkin']

1312.7658v1

Approachability theory, introduced by Blackwell (1956), provides fundamental results on repeated games with vector-valued payoffs, and has been usefully applied since in the theory of learning in games and to learning algorithms in the online adversarial setup. Given a repeated game with vector payoffs, a target set $S$ is approachable by a certain player (the agent) if he can ensure that the average payoff vector converges to that set no matter what his adversary opponent does. Blackwell provided two equivalent sets of conditions for a convex set to be approachable. The first (primary) condition is a geometric separation condition, while the second (dual) condition requires that the set be {\em non-excludable}, namely that for every mixed action of the opponent there exists a mixed action of the agent (a {\em response}) such that the resulting payoff vector belongs to $S$. Existing approachability algorithms rely on the primal condition and essentially require to compute at each stage a projection direction from a given point to $S$. In this paper, we introduce an approachability algorithm that relies on Blackwell's {\em dual} condition. Thus, rather than projection, the algorithm relies on computation of the response to a certain action of the opponent at each stage. The utility of the proposed algorithm is demonstrated by applying it to certain generalizations of the classical regret minimization problem, which include regret minimization with side constraints and regret minimization for global cost functions. In these problems, computation of the required projections is generally complex but a response is readily obtainable.

Response-Based Approachability and its Application to Generalized No-Regret Algorithms

http://arxiv.org/pdf/1312.7658v1

Title ResponseBased Approachability Application Generalized NoRegret Algorithms Summary Approachability theory introduced Blackwell 1956 provides fundamental result repeated game vectorvalued payoff usefully applied since theory learning game learning algorithm online adversarial setup Given repeated game vector payoff target set approachable certain player agent ensure average payoff vector converges set matter adversary opponent Blackwell provided two equivalent set condition convex set approachable first primary condition geometric separation condition second dual condition requires set em nonexcludable namely every mixed action opponent exists mixed action agent em response resulting payoff vector belongs Existing approachability algorithm rely primal condition essentially require compute stage projection direction given point paper introduce approachability algorithm relies Blackwells em dual condition Thus rather projection algorithm relies computation response certain action opponent stage utility proposed algorithm demonstrated applying certain generalization classical regret minimization problem include regret minimization side constraint regret minimization global cost function problem computation required projection generally complex response readily obtainable Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Andreas Veit', 'Christoph Goebel', 'Rohit Tidke', 'Christoph Doblander', 'Hans-Arno Jacobsen']

1404.0200v1

The increasing use of renewable energy sources with variable output, such as solar photovoltaic and wind power generation, calls for Smart Grids that effectively manage flexible loads and energy storage. The ability to forecast consumption at different locations in distribution systems will be a key capability of Smart Grids. The goal of this paper is to benchmark state-of-the-art methods for forecasting electricity demand on the household level across different granularities and time scales in an explorative way, thereby revealing potential shortcomings and find promising directions for future research in this area. We apply a number of forecasting methods including ARIMA, neural networks, and exponential smoothening using several strategies for training data selection, in particular day type and sliding window based strategies. We consider forecasting horizons ranging between 15 minutes and 24 hours. Our evaluation is based on two data sets containing the power usage of individual appliances at second time granularity collected over the course of several months. The results indicate that forecasting accuracy varies significantly depending on the choice of forecasting methods/strategy and the parameter configuration. Measured by the Mean Absolute Percentage Error (MAPE), the considered state-of-the-art forecasting methods rarely beat corresponding persistence forecasts. Overall, we observed MAPEs in the range between 5 and >100%. The average MAPE for the first data set was ~30%, while it was ~85% for the other data set. These results show big room for improvement. Based on the identified trends and experiences from our experiments, we contribute a detailed discussion of promising future research.

Household Electricity Demand Forecasting -- Benchmarking State-of-the-Art Methods

http://arxiv.org/pdf/1404.0200v1

Title Household Electricity Demand Forecasting Benchmarking StateoftheArt Methods Summary increasing use renewable energy source variable output solar photovoltaic wind power generation call Smart Grids effectively manage flexible load energy storage ability forecast consumption different location distribution system key capability Smart Grids goal paper benchmark stateoftheart method forecasting electricity demand household level across different granularity time scale explorative way thereby revealing potential shortcoming find promising direction future research area apply number forecasting method including ARIMA neural network exponential smoothening using several strategy training data selection particular day type sliding window based strategy consider forecasting horizon ranging 15 minute 24 hour evaluation based two data set containing power usage individual appliance second time granularity collected course several month result indicate forecasting accuracy varies significantly depending choice forecasting methodsstrategy parameter configuration Measured Mean Absolute Percentage Error MAPE considered stateoftheart forecasting method rarely beat corresponding persistence forecast Overall observed MAPEs range 5 100 average MAPE first data set 30 85 data set result show big room improvement Based identified trend experience experiment contribute detailed discussion promising future research Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Peter Banda', 'Christof Teuscher']

1404.0427v2

The current biochemical information processing systems behave in a predetermined manner because all features are defined during the design phase. To make such unconventional computing systems reusable and programmable for biomedical applications, adaptation, learning, and self-modification based on external stimuli would be highly desirable. However, so far, it has been too challenging to implement these in wet chemistries. In this paper we extend the chemical perceptron, a model previously proposed by the authors, to function as an analog instead of a binary system. The new analog asymmetric signal perceptron learns through feedback and supports Michaelis-Menten kinetics. The results show that our perceptron is able to learn linear and nonlinear (quadratic) functions of two inputs. To the best of our knowledge, it is the first simulated chemical system capable of doing so. The small number of species and reactions and their simplicity allows for a mapping to an actual wet implementation using DNA-strand displacement or deoxyribozymes. Our results are an important step toward actual biochemical systems that can learn and adapt.

Learning Two-input Linear and Nonlinear Analog Functions with a Simple Chemical System

http://arxiv.org/pdf/1404.0427v2

Title Learning Twoinput Linear Nonlinear Analog Functions Simple Chemical System Summary current biochemical information processing system behave predetermined manner feature defined design phase make unconventional computing system reusable programmable biomedical application adaptation learning selfmodification based external stimulus would highly desirable However far challenging implement wet chemistry paper extend chemical perceptron model previously proposed author function analog instead binary system new analog asymmetric signal perceptron learns feedback support MichaelisMenten kinetics result show perceptron able learn linear nonlinear quadratic function two input best knowledge first simulated chemical system capable small number specie reaction simplicity allows mapping actual wet implementation using DNAstrand displacement deoxyribozymes result important step toward actual biochemical system learn adapt Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pokkuluri Kiran Sree', 'Inampudi Ramesh Babu', 'SSSN Usha Devi N']

1404.0453v1

This paper aims at providing a survey on the problems that can be easily addressed by cellular automata in bioinformatics. Some of the authors have proposed algorithms for addressing some problems in bioinformatics but the application of cellular automata in bioinformatics is a virgin field in research. None of the researchers has tried to relate the major problems in bioinformatics and find a common solution. Extensive literature surveys were conducted. We have considered some papers in various journals and conferences for conduct of our research. This paper provides intuition towards relating various problems in bioinformatics logically and tries to attain a common frame work for addressing the same.

Cellular Automata and Its Applications in Bioinformatics: A Review

http://arxiv.org/pdf/1404.0453v1

Title Cellular Automata Applications Bioinformatics Review Summary paper aim providing survey problem easily addressed cellular automaton bioinformatics author proposed algorithm addressing problem bioinformatics application cellular automaton bioinformatics virgin field research None researcher tried relate major problem bioinformatics find common solution Extensive literature survey conducted considered paper various journal conference conduct research paper provides intuition towards relating various problem bioinformatics logically try attain common frame work addressing Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Da Kuang', 'Alex Gittens', 'Raffay Hamid']

1404.0466v2

The dominant cost in solving least-square problems using Newton's method is often that of factorizing the Hessian matrix over multiple values of the regularization parameter ($\lambda$). We propose an efficient way to interpolate the Cholesky factors of the Hessian matrix computed over a small set of $\lambda$ values. This approximation enables us to optimally minimize the hold-out error while incurring only a fraction of the cost compared to exact cross-validation. We provide a formal error bound for our approximation scheme and present solutions to a set of key implementation challenges that allow our approach to maximally exploit the compute power of modern architectures. We present a thorough empirical analysis over multiple datasets to show the effectiveness of our approach.

piCholesky: Polynomial Interpolation of Multiple Cholesky Factors for Efficient Approximate Cross-Validation

http://arxiv.org/pdf/1404.0466v2

Title piCholesky Polynomial Interpolation Multiple Cholesky Factors Efficient Approximate CrossValidation Summary dominant cost solving leastsquare problem using Newtons method often factorizing Hessian matrix multiple value regularization parameter lambda propose efficient way interpolate Cholesky factor Hessian matrix computed small set lambda value approximation enables u optimally minimize holdout error incurring fraction cost compared exact crossvalidation provide formal error bound approximation scheme present solution set key implementation challenge allow approach maximally exploit compute power modern architecture present thorough empirical analysis multiple datasets show effectiveness approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pokkuluri Kiran Sree', 'Inampudi Ramesh Babu', 'SSSN Usha Devi N']

1404.1144v1

Bioinformatics incorporates information regarding biological data storage, accessing mechanisms and presentation of characteristics within this data. Most of the problems in bioinformatics and be addressed efficiently by computer techniques. This paper aims at building a classifier based on Multiple Attractor Cellular Automata (MACA) which uses fuzzy logic with version Z to predict splicing site, protein coding and promoter region identification in eukaryotes. It is strengthened with an artificial immune system technique (AIS), Clonal algorithm for choosing rules of best fitness. The proposed classifier can handle DNA sequences of lengths 54,108,162,252,354. This classifier gives the exact boundaries of both protein and promoter regions with an average accuracy of 90.6%. This classifier can predict the splicing site with 97% accuracy. This classifier was tested with 1, 97,000 data components which were taken from Fickett & Toung , EPDnew, and other sequences from a renowned medical university.

AIS-MACA- Z: MACA based Clonal Classifier for Splicing Site, Protein Coding and Promoter Region Identification in Eukaryotes

http://arxiv.org/pdf/1404.1144v1

Title AISMACA Z MACA based Clonal Classifier Splicing Site Protein Coding Promoter Region Identification Eukaryotes Summary Bioinformatics incorporates information regarding biological data storage accessing mechanism presentation characteristic within data problem bioinformatics addressed efficiently computer technique paper aim building classifier based Multiple Attractor Cellular Automata MACA us fuzzy logic version Z predict splicing site protein coding promoter region identification eukaryote strengthened artificial immune system technique AIS Clonal algorithm choosing rule best fitness proposed classifier handle DNA sequence length 54108162252354 classifier give exact boundary protein promoter region average accuracy 906 classifier predict splicing site 97 accuracy classifier tested 1 97000 data component taken Fickett Toung EPDnew sequence renowned medical university Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Joost Broekens', 'Tim Baarslag']

1404.2078v2

Understanding the affective, cognitive and behavioural processes involved in risk taking is essential for treatment and for setting environmental conditions to limit damage. Using Temporal Difference Reinforcement Learning (TDRL) we computationally investigated the effect of optimism in risk perception in a variety of goal-oriented tasks. Optimism in risk perception was studied by varying the calculation of the Temporal Difference error, i.e., delta, in three ways: realistic (stochastically correct), optimistic (assuming action control), and overly optimistic (assuming outcome control). We show that for the gambling task individuals with 'healthy' perception of control, i.e., action optimism, do not develop gambling behaviour while individuals with 'unhealthy' perception of control, i.e., outcome optimism, do. We show that high intensity of sensations and low levels of fear co-occur due to optimistic risk perception. We found that overly optimistic risk perception (outcome optimism) results in risk taking and in persistent gambling behaviour in addition to high intensity of sensations. We discuss how our results replicate risk-taking related phenomena.

Optimistic Risk Perception in the Temporal Difference error Explains the Relation between Risk-taking, Gambling, Sensation-seeking and Low Fear

http://arxiv.org/pdf/1404.2078v2

Title Optimistic Risk Perception Temporal Difference error Explains Relation Risktaking Gambling Sensationseeking Low Fear Summary Understanding affective cognitive behavioural process involved risk taking essential treatment setting environmental condition limit damage Using Temporal Difference Reinforcement Learning TDRL computationally investigated effect optimism risk perception variety goaloriented task Optimism risk perception studied varying calculation Temporal Difference error ie delta three way realistic stochastically correct optimistic assuming action control overly optimistic assuming outcome control show gambling task individual healthy perception control ie action optimism develop gambling behaviour individual unhealthy perception control ie outcome optimism show high intensity sensation low level fear cooccur due optimistic risk perception found overly optimistic risk perception outcome optimism result risk taking persistent gambling behaviour addition high intensity sensation discus result replicate risktaking related phenomenon Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kerstin Eder', 'Chris Harper', 'Ute Leonards']

1404.2229v3

The success of the human-robot co-worker team in a flexible manufacturing environment where robots learn from demonstration heavily relies on the correct and safe operation of the robot. How this can be achieved is a challenge that requires addressing both technical as well as human-centric research questions. In this paper we discuss the state of the art in safety assurance, existing as well as emerging standards in this area, and the need for new approaches to safety assurance in the context of learning machines. We then focus on robotic learning from demonstration, the challenges these techniques pose to safety assurance and indicate opportunities to integrate safety considerations into algorithms "by design". Finally, from a human-centric perspective, we stipulate that, to achieve high levels of safety and ultimately trust, the robotic co-worker must meet the innate expectations of the humans it works with. It is our aim to stimulate a discussion focused on the safety aspects of human-in-the-loop robotics, and to foster multidisciplinary collaboration to address the research challenges identified.

Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers

http://arxiv.org/pdf/1404.2229v3

Title Towards Safety HumanintheLoop Robotics Challenges Opportunities Safety Assurance Robotic CoWorkers Summary success humanrobot coworker team flexible manufacturing environment robot learn demonstration heavily relies correct safe operation robot achieved challenge requires addressing technical well humancentric research question paper discus state art safety assurance existing well emerging standard area need new approach safety assurance context learning machine focus robotic learning demonstration challenge technique pose safety assurance indicate opportunity integrate safety consideration algorithm design Finally humancentric perspective stipulate achieve high level safety ultimately trust robotic coworker must meet innate expectation human work aim stimulate discussion focused safety aspect humanintheloop robotics foster multidisciplinary collaboration address research challenge identified Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Lee-Ad Gottlieb', 'Aryeh Kontorovich', 'Pinhas Nisnevitch']

1404.3368v3

We present the first sample compression algorithm for nearest neighbors with non-trivial performance guarantees. We complement these guarantees by demonstrating almost matching hardness lower bounds, which show that our bound is nearly optimal. Our result yields new insight into margin-based nearest neighbor classification in metric spaces and allows us to significantly sharpen and simplify existing bounds. Some encouraging empirical results are also presented.

Near-optimal sample compression for nearest neighbors

http://arxiv.org/pdf/1404.3368v3

Title Nearoptimal sample compression nearest neighbor Summary present first sample compression algorithm nearest neighbor nontrivial performance guarantee complement guarantee demonstrating almost matching hardness lower bound show bound nearly optimal result yield new insight marginbased nearest neighbor classification metric space allows u significantly sharpen simplify existing bound encouraging empirical result also presented Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Amit Daniely', 'Shai Shalev-Shwatz']

1404.3378v2

Using the recently developed framework of [Daniely et al, 2014], we show that under a natural assumption on the complexity of refuting random K-SAT formulas, learning DNF formulas is hard. Furthermore, the same assumption implies the hardness of learning intersections of $\omega(\log(n))$ halfspaces, agnostically learning conjunctions, as well as virtually all (distribution free) learning problems that were previously shown hard (under complexity assumptions).

Complexity theoretic limitations on learning DNF's

http://arxiv.org/pdf/1404.3378v2

Title Complexity theoretic limitation learning DNFs Summary Using recently developed framework Daniely et al 2014 show natural assumption complexity refuting random KSAT formula learning DNF formula hard Furthermore assumption implies hardness learning intersection omegalogn halfspaces agnostically learning conjunction well virtually distribution free learning problem previously shown hard complexity assumption Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Karthik Raman', 'Thorsten Joachims']

1404.3656v1

MOOCs have the potential to revolutionize higher education with their wide outreach and accessibility, but they require instructors to come up with scalable alternates to traditional student evaluation. Peer grading -- having students assess each other -- is a promising approach to tackling the problem of evaluation at scale, since the number of "graders" naturally scales with the number of students. However, students are not trained in grading, which means that one cannot expect the same level of grading skills as in traditional settings. Drawing on broad evidence that ordinal feedback is easier to provide and more reliable than cardinal feedback, it is therefore desirable to allow peer graders to make ordinal statements (e.g. "project X is better than project Y") and not require them to make cardinal statements (e.g. "project X is a B-"). Thus, in this paper we study the problem of automatically inferring student grades from ordinal peer feedback, as opposed to existing methods that require cardinal peer feedback. We formulate the ordinal peer grading problem as a type of rank aggregation problem, and explore several probabilistic models under which to estimate student grades and grader reliability. We study the applicability of these methods using peer grading data collected from a real class -- with instructor and TA grades as a baseline -- and demonstrate the efficacy of ordinal feedback techniques in comparison to existing cardinal peer grading methods. Finally, we compare these peer-grading techniques to traditional evaluation techniques.

Methods for Ordinal Peer Grading

http://arxiv.org/pdf/1404.3656v1

Title Methods Ordinal Peer Grading Summary MOOCs potential revolutionize higher education wide outreach accessibility require instructor come scalable alternate traditional student evaluation Peer grading student ass promising approach tackling problem evaluation scale since number grader naturally scale number student However student trained grading mean one cannot expect level grading skill traditional setting Drawing broad evidence ordinal feedback easier provide reliable cardinal feedback therefore desirable allow peer grader make ordinal statement eg project X better project require make cardinal statement eg project X B Thus paper study problem automatically inferring student grade ordinal peer feedback opposed existing method require cardinal peer feedback formulate ordinal peer grading problem type rank aggregation problem explore several probabilistic model estimate student grade grader reliability study applicability method using peer grading data collected real class instructor TA grade baseline demonstrate efficacy ordinal feedback technique comparison existing cardinal peer grading method Finally compare peergrading technique traditional evaluation technique Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Vitaly Feldman', 'Pravesh Kothari', 'Jan Vondrák']

1404.4702v2

We study the complexity of learning and approximation of self-bounding functions over the uniform distribution on the Boolean hypercube ${0,1}^n$. Informally, a function $f:{0,1}^n \rightarrow \mathbb{R}$ is self-bounding if for every $x \in {0,1}^n$, $f(x)$ upper bounds the sum of all the $n$ marginal decreases in the value of the function at $x$. Self-bounding functions include such well-known classes of functions as submodular and fractionally-subadditive (XOS) functions. They were introduced by Boucheron et al. in the context of concentration of measure inequalities. Our main result is a nearly tight $\ell_1$-approximation of self-bounding functions by low-degree juntas. Specifically, all self-bounding functions can be $\epsilon$-approximated in $\ell_1$ by a polynomial of degree $\tilde{O}(1/\epsilon)$ over $2^{\tilde{O}(1/\epsilon)}$ variables. We show that both the degree and junta-size are optimal up to logarithmic terms. Previous techniques considered stronger $\ell_2$ approximation and proved nearly tight bounds of $\Theta(1/\epsilon^{2})$ on the degree and $2^{\Theta(1/\epsilon^2)}$ on the number of variables. Our bounds rely on the analysis of noise stability of self-bounding functions together with a stronger connection between noise stability and $\ell_1$ approximation by low-degree polynomials. This technique can also be used to get tighter bounds on $\ell_1$ approximation by low-degree polynomials and faster learning algorithm for halfspaces. These results lead to improved and in several cases almost tight bounds for PAC and agnostic learning of self-bounding functions relative to the uniform distribution. In particular, assuming hardness of learning juntas, we show that PAC and agnostic learning of self-bounding functions have complexity of $n^{\tilde{\Theta}(1/\epsilon)}$.

Nearly Tight Bounds on $\ell_1$ Approximation of Self-Bounding Functions

http://arxiv.org/pdf/1404.4702v2

Title Nearly Tight Bounds ell1 Approximation SelfBounding Functions Summary study complexity learning approximation selfbounding function uniform distribution Boolean hypercube 01n Informally function f01n rightarrow mathbbR selfbounding every x 01n fx upper bound sum n marginal decrease value function x Selfbounding function include wellknown class function submodular fractionallysubadditive XOS function introduced Boucheron et al context concentration measure inequality main result nearly tight ell1approximation selfbounding function lowdegree junta Specifically selfbounding function epsilonapproximated ell1 polynomial degree tildeO1epsilon 2tildeO1epsilon variable show degree juntasize optimal logarithmic term Previous technique considered stronger ell2 approximation proved nearly tight bound Theta1epsilon2 degree 2Theta1epsilon2 number variable bound rely analysis noise stability selfbounding function together stronger connection noise stability ell1 approximation lowdegree polynomial technique also used get tighter bound ell1 approximation lowdegree polynomial faster learning algorithm halfspaces result lead improved several case almost tight bound PAC agnostic learning selfbounding function relative uniform distribution particular assuming hardness learning junta show PAC agnostic learning selfbounding function complexity ntildeTheta1epsilon Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Orly Avner', 'Shie Mannor']

1404.5421v1

We consider the problem of multiple users targeting the arms of a single multi-armed stochastic bandit. The motivation for this problem comes from cognitive radio networks, where selfish users need to coexist without any side communication between them, implicit cooperation or common control. Even the number of users may be unknown and can vary as users join or leave the network. We propose an algorithm that combines an $\epsilon$-greedy learning rule with a collision avoidance mechanism. We analyze its regret with respect to the system-wide optimum and show that sub-linear regret can be obtained in this setting. Experiments show dramatic improvement compared to other algorithms for this setting.

Concurrent bandits and cognitive radio networks

http://arxiv.org/pdf/1404.5421v1

Title Concurrent bandit cognitive radio network Summary consider problem multiple user targeting arm single multiarmed stochastic bandit motivation problem come cognitive radio network selfish user need coexist without side communication implicit cooperation common control Even number user may unknown vary user join leave network propose algorithm combine epsilongreedy learning rule collision avoidance mechanism analyze regret respect systemwide optimum show sublinear regret obtained setting Experiments show dramatic improvement compared algorithm setting Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ran Zhao', 'Deanna Needell', 'Christopher Johansen', 'Jerry L. Grenard']

1404.5899v1

In this paper, we compare and analyze clustering methods with missing data in health behavior research. In particular, we propose and analyze the use of compressive sensing's matrix completion along with spectral clustering to cluster health related data. The empirical tests and real data results show that these methods can outperform standard methods like LPA and FIML, in terms of lower misclassification rates in clustering and better matrix completion performance in missing data problems. According to our examination, a possible explanation of these improvements is that spectral clustering takes advantage of high data dimension and compressive sensing methods utilize the near-to-low-rank property of health data.

A Comparison of Clustering and Missing Data Methods for Health Sciences

http://arxiv.org/pdf/1404.5899v1

Title Comparison Clustering Missing Data Methods Health Sciences Summary paper compare analyze clustering method missing data health behavior research particular propose analyze use compressive sensing matrix completion along spectral clustering cluster health related data empirical test real data result show method outperform standard method like LPA FIML term lower misclassification rate clustering better matrix completion performance missing data problem According examination possible explanation improvement spectral clustering take advantage high data dimension compressive sensing method utilize neartolowrank property health data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zongyan Huang', 'Matthew England', 'David Wilson', 'James H. Davenport', 'Lawrence C. Paulson', 'James Bridge']

1404.6369v1

Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

http://arxiv.org/pdf/1404.6369v1

Title Applying machine learning problem choosing heuristic select variable ordering cylindrical algebraic decomposition Summary Cylindrical algebraic decompositionCAD key tool computational algebraic geometry particularly quantifier elimination realclosed field using CAD often choice ordering placed variable important problem infeasible one variable ordering easy another Machine learning process fitting computer model complex function based property learned measured data paper use machine learning specifically support vector machine select heuristic choosing variable ordering outperforming separate heuristic Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Imen Trabelsi', 'Dorra Ben Ayed']

1407.0380v1

Several speaker identification systems are giving good performance with clean speech but are affected by the degradations introduced by noisy audio conditions. To deal with this problem, we investigate the use of complementary information at different levels for computing a combined match score for the unknown speaker. In this work, we observe the effect of two supervised machine learning approaches including support vectors machines (SVM) and na\"ive bayes (NB). We define two feature vector sets based on mel frequency cepstral coefficients (MFCC) and relative spectral perceptual linear predictive coefficients (RASTA-PLP). Each feature is modeled using the Gaussian Mixture Model (GMM). Several ways of combining these information sources give significant improvements in a text-independent speaker identification task using a very large telephone degraded NTIMIT database.

A Multi Level Data Fusion Approach for Speaker Identification on Telephone Speech

http://arxiv.org/pdf/1407.0380v1

Title Multi Level Data Fusion Approach Speaker Identification Telephone Speech Summary Several speaker identification system giving good performance clean speech affected degradation introduced noisy audio condition deal problem investigate use complementary information different level computing combined match score unknown speaker work observe effect two supervised machine learning approach including support vector machine SVM naive bayes NB define two feature vector set based mel frequency cepstral coefficient MFCC relative spectral perceptual linear predictive coefficient RASTAPLP feature modeled using Gaussian Mixture Model GMM Several way combining information source give significant improvement textindependent speaker identification task using large telephone degraded NTIMIT database Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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