LIBSVM: A library for support vector machines

doi:10.1145/1961189.1961199

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LIBSVM: A library for support vector machines

Journal Article•DOI•

LIBSVM: A library for support vector machines

Chih-Chung Chang¹, Chih-Jen Lin¹•Institutions (1)

National Taiwan University¹

06 May 2011-ACM Transactions on Intelligent Systems and Technology (ACM)-Vol. 2, Iss: 3, pp 27

TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

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Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

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Proceedings Article•

Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors

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Faiyaz Al Zamal¹, Wendy Liu¹, Derek Ruths¹•Institutions (1)

McGill University¹

20 May 2012

TL;DR: This paper evaluates the inference accuracy gained by augmenting the user features with features derived from the Twitter profiles and postings of her friends, and considers three attributes which have varying degrees of assortativity: gender, age, and political affiliation.

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Abstract: In this paper, we extend existing work on latent attribute inference by leveraging the principle of homophily: we evaluate the inference accuracy gained by augmenting the user features with features derived from the Twitter profiles and postings of her friends. We consider three attributes which have varying degrees of assortativity: gender, age, and political affiliation. Our approach yields a significant and robust increase in accuracy for both age and political affiliation, indicating that our approach boosts performance for attributes with moderate to high assortativity. Furthermore, different neighborhood subsets yielded optimal performance for different attributes, suggesting that different subsamples of the user's neighborhood characterize different aspects of the user herself. Finally, inferences using only the features of a user's neighbors outperformed those based on the user's features alone. This suggests that the neighborhood context alone carries substantial information about the user.

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349 citations

Proceedings Article•DOI•

Ordinal hyperplanes ranker with cost sensitivities for age estimation

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Kuang-Yu Chang¹, Chu-Song Chen¹, Yi-Ping Hung¹•Institutions (1)

Academia Sinica¹

20 Jun 2011

TL;DR: The experimental results demonstrate that the proposed approach outperforms conventional multiclass-based and regression-based approaches as well as recently developed ranking-based age estimation approaches.

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Abstract: In this paper, we propose an ordinal hyperplane ranking algorithm called OHRank, which estimates human ages via facial images. The design of the algorithm is based on the relative order information among the age labels in a database. Each ordinal hyperplane separates all the facial images into two groups according to the relative order, and a cost-sensitive property is exploited to find better hyperplanes based on the classification costs. Human ages are inferred by aggregating a set of preferences from the ordinal hyperplanes with their cost sensitivities. Our experimental results demonstrate that the proposed approach outperforms conventional multiclass-based and regression-based approaches as well as recently developed ranking-based age estimation approaches.

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349 citations

Cites methods from "LIBSVM: A library for support vecto..."

...However, we simply apply cross validation to select a single kernel for each subproblem, and use LIBSVM [3] in our implementation....
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...In SVR and SVM learning, LIBSVM [3] is used to evaluate the approaches....
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Journal Article•DOI•

Short-term emotion assessment in a recall paradigm

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Guillaume Chanel¹, Joep J. M. Kierkels¹, Mohammad Soleymani¹, Thierry Pun¹•Institutions (1)

University of Geneva¹

01 Aug 2009-International Journal of Human-computer Studies \/ International Journal of Man-machine Studies

TL;DR: Comparison of results obtained using either peripheral or EEG signals confirms the interest of using EEGs to assess valence and arousal in emotion recall conditions.

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Abstract: The work presented in this paper aims at assessing human emotions using peripheral as well as electroencephalographic (EEG) physiological signals on short-time periods Three specific areas of the valence-arousal emotional space are defined, corresponding to negatively excited, positively excited, and calm-neutral states An acquisition protocol based on the recall of past emotional life episodes has been designed to acquire data from both peripheral and EEG signals Pattern classification is used to distinguish between the three areas of the valence-arousal space The performance of several classifiers has been evaluated on 10 participants and different feature sets: peripheral features, EEG time-frequency features, EEG pairwise mutual information (MI) features Comparison of results obtained using either peripheral or EEG signals confirms the interest of using EEGs to assess valence and arousal in emotion recall conditions The obtained accuracy for the three emotional classes is 63% using EEG time-frequency features, which is better than the results obtained from previous studies using EEG and similar classes Fusion of the different feature sets at the decision level using a summation rule also showed to improve accuracy to 70% Furthermore, the rejection of non-confident samples finally led to a classification accuracy of 80% for the three classes

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349 citations

Proceedings Article•DOI•

Top-down and bottom-up cues for scene text recognition

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Anand Mishra¹, Karteek Alahari², C. V. Jawahar¹•Institutions (2)

International Institute of Information Technology, Hyderabad¹, École Normale Supérieure²

16 Jun 2012

TL;DR: This work presents a framework that exploits both bottom-up and top-down cues in the problem of recognizing text extracted from street images, and shows significant improvements in accuracies on two challenging public datasets, namely Street View Text and ICDAR 2003.

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Abstract: Scene text recognition has gained significant attention from the computer vision community in recent years. Recognizing such text is a challenging problem, even more so than the recognition of scanned documents. In this work, we focus on the problem of recognizing text extracted from street images. We present a framework that exploits both bottom-up and top-down cues. The bottom-up cues are derived from individual character detections from the image. We build a Conditional Random Field model on these detections to jointly model the strength of the detections and the interactions between them. We impose top-down cues obtained from a lexicon-based prior, i.e. language statistics, on the model. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We show significant improvements in accuracies on two challenging public datasets, namely Street View Text (over 15%) and ICDAR 2003 (nearly 10%).

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349 citations

Cites methods from "LIBSVM: A library for support vecto..."

...We observed that all these potential character windows were missed due to poor SVM scores....
[...]
...On the other hand, false windows, which either have poor SVM scores or vary from the average aspect ratio size or both will be more likely to take the null label ǫ....
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...For a true window, which has a relatively good SVM score, and matches the average aspect ratio size, this cost of assigning a null label is high....
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...Some of the typical failures are due to poor resolution of the images, which leads to very weak SVM scores, as shown in Figure 5....
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...We learnt a 1-vs-all SVM classifier with an RBF kernel using these features....
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Journal Article•DOI•

Identification of MCI individuals using structural and functional connectivity networks

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Chong Yaw Wee¹, Pew Thian Yap¹, Daoqiang Zhang¹, Kevin Denny², Jeffrey N. Browndyke², Guy G. Potter², Kathleen A. Welsh-Bohmer², Lihong Wang², Dinggang Shen¹ - Show less +5 more•Institutions (2)

University of North Carolina at Chapel Hill¹, Duke University²

01 Feb 2012-NeuroImage

TL;DR: This study attempts to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance and indicates that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently.

...read moreread less

349 citations

Cites methods from "LIBSVM: A library for support vecto..."

...;X mð ÞÞ as a mixed-kernel between the multimodality training sample between Xi and the test sample X. SVM training and classification A linear kernel based SVM classifier based on the LIBSVM library (Chang and Lin, 2001) was adopted in the current framework based on mixed-kernel....
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...SVM training and classification A linear kernel based SVM classifier based on the LIBSVM library (Chang and Lin, 2001) was adopted in the current framework based on mixed-kernel....
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References

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Journal Article•DOI•

Support-Vector Networks

[...]

Corinna Cortes¹, Vladimir Vapnik¹•Institutions (1)

Bell Labs¹

15 Sep 1995-Machine Learning

TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

...read moreread less

Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

...read moreread less

37,861 citations

"LIBSVM: A library for support vecto..." refers background in this paper

...{1,-1}, C-SVC [Boser et al. 1992; Cortes and Vapnik 1995] solves 4LIBSVM Tools: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools. the following primal optimization problem: l t min 1 w T w +C .i (1) w,b,. 2 i=1 subject to yi(w T f(xi) +b) =1 -.i, .i =0,i =1,...,l, where f(xi)maps xi into a…...
[...]

Statistical learning theory

[...]

Vladimir Vapnik

01 Jan 1998

TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

...read moreread less

Abstract: A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

...read moreread less

26,531 citations

"LIBSVM: A library for support vecto..." refers background in this paper

...Under given parameters C > 0and E> 0, the standard form of support vector regression [Vapnik 1998] is ll tt 1 T min w w + C .i + C .i * w,b,.,. * 2 i=1 i=1 subject to w T f(xi) + b- zi = E + .i, zi - w T f(xi) - b = E + .i * , * .i,.i = 0,i = 1,...,l....
[...]
...It can be clearly seen that C-SVC and one-class SVM are already in the form of problem (11)....
[...]
..., l, in two classes, and a vector y ∈ Rl such that yi ∈ {1,−1}, C-SVC (Cortes and Vapnik, 1995; Vapnik, 1998) solves the following primal problem:...
[...]
...Then, according to the SVM formulation, svm train one calls a corresponding subroutine such as solve c svc for C-SVC and solve nu svc for ....
[...]
...Note that b of C-SVC and E-SVR plays the same role as -. in one-class SVM, so we de.ne ....
[...]

Proceedings Article•DOI•

A training algorithm for optimal margin classifiers

[...]

Bernhard E. Boser¹, Isabelle Guyon², Vladimir Vapnik²•Institutions (2)

University of California, Berkeley¹, Bell Labs²

01 Jul 1992

TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.

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Abstract: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.

...read moreread less

11,211 citations

"LIBSVM: A library for support vecto..." refers background in this paper

...It can be clearly seen that C-SVC and one-class SVM are already in the form of problem (11)....
[...]
...Then, according to the SVM formulation, svm train one calls a corresponding subroutine such as solve c svc for C-SVC and solve nu svc for ....
[...]
...Note that b of C-SVC and E-SVR plays the same role as -. in one-class SVM, so we de.ne ....
[...]
...In Section 2, we describe SVM formulations supported in LIBSVM: C-Support Vector Classi.cation (C-SVC), ....
[...]
...{1,-1}, C-SVC [Boser et al. 1992; Cortes and Vapnik 1995] solves 4LIBSVM Tools: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools. the following primal optimization problem: l t min 1 w T w +C .i (1) w,b,. 2 i=1 subject to yi(w T f(xi) +b) =1 -.i, .i =0,i =1,...,l, where f(xi)maps xi into a higher-dimensional space and C > 0 is the regularization parameter....
[...]

A Practical Guide to Support Vector Classication

[...]

Hsu Chih-Wei, Chih-Chung Chang¹, Chih-Jen Lin•Institutions (1)

National Taiwan University¹

01 Jan 2008

TL;DR: A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.

...read moreread less

Abstract: Support vector machine (SVM) is a popular technique for classication. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but signicant steps. In this guide, we propose a simple procedure, which usually gives reasonable results.

...read moreread less

7,069 citations

"LIBSVM: A library for support vecto..." refers methods in this paper

...A Simple Example of Running LIBSVM While detailed instructions of using LIBSVM are available in the README file of the package and the practical guide by Hsu et al. [2003], here we give a simple example....
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...For instructions of using LIBSVM, see the README file included in the package, the LIBSVM FAQ,3 and the practical guide by Hsu et al. [2003]. LIBSVM supports the following learning tasks....
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Journal Article•DOI•

A comparison of methods for multiclass support vector machines

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Hsu Chih-Wei¹, Chih-Jen Lin¹•Institutions (1)

National Taiwan University¹

01 Mar 2002-IEEE Transactions on Neural Networks

TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.

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Abstract: Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.

...read moreread less

6,562 citations