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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Journal Article•DOI•

No-Reference Image Quality Assessment Using Visual Codebooks

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Peng Ye¹, David Doermann¹•Institutions (1)

University of Maryland, College Park¹

01 Jul 2012-IEEE Transactions on Image Processing

TL;DR: The proposed method is comparable to state-of-the-art general-purpose NR-IQA methods and outperforms the full-reference image quality metrics, peak signal-to-noise ratio and structural similarity index on the Laboratory for Image and Video Engineering IQA database.

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Abstract: The goal of no-reference objective image quality assessment (NR-IQA) is to develop a computational model that can predict the human-perceived quality of distorted images accurately and automatically without any prior knowledge of reference images. Most existing NR-IQA approaches are distortion specific and are typically limited to one or two specific types of distortions. In most practical applications, however, information about the distortion type is not really available. In this paper, we propose a general-purpose NR-IQA approach based on visual codebooks. A visual codebook consisting of Gabor-filter-based local features extracted from local image patches is used to capture complex statistics of a natural image. The codebook encodes statistics by quantizing the feature space and accumulating histograms of patch appearances. This method does not assume any specific types of distortions; however, when evaluating images with a particular type of distortion, it does require examples with the same or similar distortion for training. Experimental results demonstrate that the predicted quality score using our method is consistent with human-perceived image quality. The proposed method is comparable to state-of-the-art general-purpose NR-IQA methods and outperforms the full-reference image quality metrics, peak signal-to-noise ratio and structural similarity index on the Laboratory for Image and Video Engineering IQA database.

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260 citations

Additional excerpts

...We only report results on the four distortions—JPEG2k, JPEG, WN, and BLUR—that are present in both the LIVE database and the CSIQ database....
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Posted Content•

Efficient Learning of Domain-invariant Image Representations

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Judy Hoffman¹, Erik Rodner¹, Jeff Donahue¹, Trevor Darrell¹, Kate Saenko² - Show less +1 more•Institutions (2)

University of California, Berkeley¹, University of Massachusetts Lowell²

15 Jan 2013-arXiv: Learning

TL;DR: An algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers is presented, which combines several features previously unavailable in a single algorithm.

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Abstract: We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches.

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260 citations

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

...We used the LIBSVM package [26] for kernelized methods and Liblinear [27] package for linear methods....
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Journal Article•DOI•

High-Resolution SAR Image Classification via Deep Convolutional Autoencoders

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Jie Geng¹, Jianchao Fan, Hongyu Wang¹, Xiaorui Ma¹, Baoming Li, Fuliang Chen - Show less +2 more•Institutions (1)

Dalian University of Technology¹

01 Oct 2015-IEEE Geoscience and Remote Sensing Letters

TL;DR: The experiments of TerraSAR-X image demonstrate that the DCAE network can extract efficient features and perform better classification result compared with some related algorithms.

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Abstract: Synthetic aperture radar (SAR) image classification is a hot topic in the interpretation of SAR images. However, the absence of effective feature representation and the presence of speckle noise in SAR images make classification difficult to handle. In order to overcome these problems, a deep convolutional autoencoder (DCAE) is proposed to extract features and conduct classification automatically. The deep network is composed of eight layers: a convolutional layer to extract texture features, a scale transformation layer to aggregate neighbor information, four layers based on sparse autoencoders to optimize features and classify, and last two layers for postprocessing. Compared with hand-crafted features, the DCAE network provides an automatic method to learn discriminative features from the image. A series of filters is designed as convolutional units to comprise the gray-level cooccurrence matrix and Gabor features together. Scale transformation is conducted to reduce the influence of the noise, which integrates the correlated neighbor pixels. Sparse autoencoders seek better representation of features to match the classifier, since training labels are added to fine-tune the parameters of the networks. Morphological smoothing removes the isolated points of the classification map. The whole network is designed ingeniously, and each part has a contribution to the classification accuracy. The experiments of TerraSAR-X image demonstrate that the DCAE network can extract efficient features and perform better classification result compared with some related algorithms.

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260 citations

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

...The support vector machine (SVM) [4], [6], sparse representation classifier (SRC) [7], [8], and extreme learning machine [9] have been widely applied in the SAR image classification....
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...In the SVM method, we apply RBF kernel and use grid-search with tenfold cross validation to obtain the parameters, which are a regularization constant C and a kernel hyperparameter γ....
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...We compare our proposed method with other three off-theshelf approaches, which are SVM [6], SRC [7], and SAE [15], respectively....
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Journal Article•DOI•

BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches

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Bin Liu

19 Jul 2019-Briefings in Bioinformatics

TL;DR: Experimental results showed that the predictors generated by BioSeq-Analysis even outperformed some state-of-the-art methods and will become a useful tool for biological sequence analysis.

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Abstract: With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze their structures and functions. Machine learning techniques are playing key roles in this field. Typically, predictors based on machine learning techniques contain three main steps: feature extraction, predictor construction and performance evaluation. Although several Web servers and stand-alone tools have been developed to facilitate the biological sequence analysis, they only focus on individual step. In this regard, in this study a powerful Web server called BioSeq-Analysis (http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/) has been proposed to automatically complete the three main steps for constructing a predictor. The user only needs to upload the benchmark data set. BioSeq-Analysis can generate the optimized predictor based on the benchmark data set, and the performance measures can be reported as well. Furthermore, to maximize user's convenience, its stand-alone program was also released, which can be downloaded from http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/download/, and can be directly run on Windows, Linux and UNIX. Applied to three sequence analysis tasks, experimental results showed that the predictors generated by BioSeq-Analysis even outperformed some state-of-the-art methods. It is anticipated that BioSeq-Analysis will become a useful tool for biological sequence analysis.

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260 citations

Journal Article•DOI•

Visual object-action recognition: Inferring object affordances from human demonstration

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Hedvig Kjellström, Javier Romero, Danica Kragic

01 Jan 2011-Computer Vision and Image Understanding

TL;DR: A method is presented for categorizing manipulated objects and human manipulation actions in context of each other, able to simultaneously segment and classify human hand actions, and detect and classify the objects involved in the action.

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259 citations

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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.

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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

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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