LIBSVM: A library for support vector machines

doi:10.1145/1961189.1961199

Home
/
Papers
/
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.

read less

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.

...read moreread less

Content maybe subject to copyright Report

Citations

PDF

Open Access

More filters

Patent•

Methods and processes for non-invasive assessment of genetic variations

[...]

Cosmin Deciu¹, Zeljko Dzakula¹•Institutions (1)

Sequenom, Inc.¹

01 Mar 2013

TL;DR: In this paper, methods, processes and apparatuses for non-invasive assessment of genetic variations are described and discussed. But none of them are suitable for noninvasive testing.

...read moreread less

Abstract: Provided herein are methods, processes and apparatuses for non-invasive assessment of genetic variations.

...read moreread less

231 citations

Journal Article•DOI•

Sequence and chromatin determinants of cell-type–specific transcription factor binding

[...]

Aaron Arvey¹, Phaedra Agius¹, William Stafford Noble², Christina S. Leslie¹•Institutions (2)

Memorial Sloan Kettering Cancer Center¹, University of Washington²

01 Sep 2012-Genome Research

TL;DR: The results suggest that using a single motif for each TF and filtering for chromatin accessible loci is not always sufficient to accurately account for cell-type-specific binding profiles, and cell- type-specific sequence models are better able to explain differential binding.

...read moreread less

Abstract: Gene regulatory programs in distinct cell types are maintained in large part through the cell-type–specific binding of transcription factors (TFs). The determinants of TF binding include direct DNA sequence preferences, DNA sequence preferences of cofactors, and the local cell-dependent chromatin context. To explore the contribution of DNA sequence signal, histone modifications, and DNase accessibility to cell-type–specific binding, we analyzed 286 ChIP-seq experiments performed by the ENCODE Consortium. This analysis included experiments for 67 transcriptional regulators, 15 of which were profiled in both the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines. To model TF-bound regions, we trained support vector machines (SVMs) that use flexible k-mer patterns to capture DNA sequence signals more accurately than traditional motif approaches. In addition, we trained SVM spatial chromatin signatures to model local histone modifications and DNase accessibility, obtaining significantly more accurate TF occupancy predictions than simpler approaches. Consistent with previous studies, we find that DNase accessibility can explain cell-line–specific binding for many factors. However, we also find that of the 10 factors with prominent cell-type–specific binding patterns, four display distinct cell-type–specific DNA sequence preferences according to our models. Moreover, for two factors we identify cell-specific binding sites that are accessible in both cell types but bound only in one. For these sites, cell-type–specific sequence models, rather than DNase accessibility, are better able to explain differential binding. Our results suggest that using a single motif for each TF and filtering for chromatin accessible loci is not always sufficient to accurately account for cell-type–specific binding profiles.

...read moreread less

231 citations

Journal Article•DOI•

Optimal feature selection for support vector machines

[...]

Minh Hoai Nguyen¹, Fernando De la Torre¹•Institutions (1)

Carnegie Mellon University¹

01 Mar 2010-Pattern Recognition

TL;DR: This paper proposes a convex energy-based framework to jointly perform feature selection and SVM parameter learning for linear and non-linear kernels.

...read moreread less

231 citations

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

...Hermes and Buhmann’s method is much faster than the other methods because it is based on LibSVM [33], a highly customized optimization package....
[...]
...For RELIEF, we used the implementation bundled with Weka [32], a java-based machine learning toolkit.4 Normal SVM classifiers were built using LibSVM [33]....
[...]

Proceedings Article•DOI•

Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning

[...]

Jun Inoue¹, Yoriyuki Yamagata², Yuqi Chen, Christopher M. Poskitt³, Jun Sun⁴ - Show less +1 more•Institutions (4)

Tokyo Denki University¹, National Institute of Advanced Industrial Science and Technology², Singapore University of Technology and Design³, Shenzhen University⁴

01 Nov 2017

TL;DR: This paper proposes and evaluates the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS), and compares two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM).

...read moreread less

Abstract: In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully operational raw water purification plant. For both methods, we first train detectors using a log generated by SWaT operating under normal conditions. Then, we evaluate the performance of both methods using a log generated by SWaT operating under 36 different attack scenarios. We find that our DNN generates fewer false positives than our one-class SVM while our SVM detects slightly more anomalies. Overall, our DNN has a slightly better F measure than our SVM. We discuss the characteristics of the DNN and one-class SVM used in this experiment, and compare the advantages and disadvantages of the two methods.

...read moreread less

231 citations

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

...learn machine learning library [26], which uses libsvm [27]...
[...]

Proceedings Article•DOI•

Question Answering on Freebase via Relation Extraction and Textual Evidence

[...]

Kun Xu¹, Siva Reddy², Yansong Feng³, Songfang Huang⁴, Dongyan Zhao³ - Show less +1 more•Institutions (4)

Beijing University of Posts and Telecommunications¹, University of Edinburgh², Peking University³, IBM⁴

01 Aug 2016

TL;DR: The authors used a neural network based relation extractor to retrieve candidate answers from Freebase, and then infer over Wikipedia to validate these answers, achieving an F_1 of 53.3% on the WebQuestions question answering dataset.

...read moreread less

Abstract: Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a neural network based relation extractor to retrieve the candidate answers from Freebase, and then infer over Wikipedia to validate these answers. Experiments on the WebQuestions question answering dataset show that our method achieves an F_1 of 53.3%, a substantial improvement over the state-of-the-art.

...read moreread less

231 citations

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
…
153
154
155
156
157
158
159
…
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200

Collapse

References

PDF

Open Access

More filters

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.

...read moreread less

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....
[...]
...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....
[...]

Journal Article•DOI•

A comparison of methods for multiclass support vector machines

[...]

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.

...read moreread less

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