LIBSVM: A library for support vector machines
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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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TL;DR: Comprehensive experiments on three domain adaptation data sets demonstrate that DTMKL-based methods outperform existing cross-domain learning and multiple kernel learning methods.
Abstract: Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. To cope with the considerable change between feature distributions of different domains, we propose a new cross-domain kernel learning framework into which many existing kernel methods can be readily incorporated. Our framework, referred to as Domain Transfer Multiple Kernel Learning (DTMKL), simultaneously learns a kernel function and a robust classifier by minimizing both the structural risk functional and the distribution mismatch between the labeled and unlabeled samples from the auxiliary and target domains. Under the DTMKL framework, we also propose two novel methods by using SVM and prelearned classifiers, respectively. Comprehensive experiments on three domain adaptation data sets (i.e., TRECVID, 20 Newsgroups, and email spam data sets) demonstrate that DTMKL-based methods outperform existing cross-domain learning and multiple kernel learning methods.
562 citations
TL;DR: From experimental results, it is found that power spectrum feature is superior to other two kinds of features; a linear dynamic system based feature smoothing method can significantly improve emotion classification accuracy; and the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning.
561 citations
TL;DR: A novel supervised inference method, known as bipartite local models to first predict target proteins of a given drug, then to predict drugs targeting a given protein, which gives two independent predictions for each putative drug–target interaction, which can be combined to give a definitive prediction for each interaction.
Abstract: Motivation:In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs for known diseases. This problem is currently being attacked from many different points of view, a strong indication of its current importance. Precisely, being able to predict new drug–target interactions with both high precision and accuracy is the holy grail, a fundamental requirement for in silico methods to be useful in a biological setting. This, however, remains extremely challenging due to, amongst other things, the rarity of known drug–target interactions.
Results: We propose a novel supervised inference method to predict unknown drug–target interactions, represented as a bipartite graph. We use this method, known as bipartite local models to first predict target proteins of a given drug, then to predict drugs targeting a given protein. This gives two independent predictions for each putative drug–target interaction, which we show can be combined to give a definitive prediction for each interaction. We demonstrate the excellent performance of the proposed method in the prediction of four classes of drug–target interaction networks involving enzymes, ion channels, G protein-coupled receptors (GPCRs) and nuclear receptors in human. This enables us to suggest a number of new potential drug–target interactions.
Availability: An implementation of the proposed algorithm is available upon request from the authors. Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/bipartitelocal/.
Contact: kevbleakley@gmail.com
Supplementary information:Supplementary data are available at Bioinformatics online.
558 citations
Cites background from "LIBSVM: A library for support vecto..."
...One of the most commonly used is docking simulations (Cheng et al., 2007; Rarey et al., 1996). This is relevant when the 3D structure of a protein is already known, which is unfortunately not often the case, limiting large-scale implementation. Keiser et al. (2007) provided a method to predict target protein families based on the known structures of a set of ligands....
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TL;DR: It is found that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects, and are seen as an important step toward noninvasive diagnostic decision support in PD.
Abstract: There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.
558 citations
Cites methods from "LIBSVM: A library for support vecto..."
...Here, we used the LIBSVM implementation [33] and followed the suggestions of the developers of that implementation [34]: we linearly scaled each of the input features to lie in the range [–1, 1], and used a Gaussian, radial basis function kernel....
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Proceedings Article•
01 May 2012TL;DR: The general architecture of the library is described, the major changes and improvements included in FreeLing version 3.0 are presented, and some relevant industrial projects in which it has been used are summarized.
Abstract: FreeLing is an open-source multilingual language processing library providing a wide range of analyzers for several languages. It offers text processing and language annotation facilities to NLP application developers, lowering the cost of building those applications. FreeLing is customizable, extensible, and has a strong orientation to real-world applications in terms of speed and robustness. Developers can use the default linguistic resources (dictionaries, lexicons, grammars, etc.), extend/adapt them to specific domains, or --since the library is open source-- develop new ones for specific languages or special application needs. This paper describes the general architecture of the library, presents the major changes and improvements included in FreeLing version 3.0, and summarizes some relevant industrial projects in which it has been used.
555 citations
References
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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.
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.
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…...
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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.
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.
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....
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...It can be clearly seen that C-SVC and one-class SVM are already in the form of problem (11)....
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..., 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:...
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...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 ....
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...Note that b of C-SVC and E-SVR plays the same role as -. in one-class SVM, so we de.ne ....
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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.
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.
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 ....
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...In Section 2, we describe SVM formulations supported in LIBSVM: C-Support Vector Classi.cation (C-SVC), ....
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...{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....
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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.
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.
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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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.
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.
6,562 citations