LIBSVM -- A Library for Support Vector Machines

Chih-Chung Chang and Chih-Jen Lin

Introduction

LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

Since version 2.8, it implements an SMO-type algorithm proposed in this paper:

R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM)

Our goal is to help users from other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include

Different SVM formulations

Efficient multi-class classification

Cross validation for model selection

Probability estimates

Various kernels (including precomputed kernel matrix)

Weighted SVM for unbalanced data

Both C++ and Java sources

GUI demonstrating SVM classification and regression

Python, R, MATLAB, Perl, Ruby, Weka, Common LISP, CLISP, Haskell, OCaml, LabVIEW, and PHP interfaces. C# .NET code and CUDA extension is available.

It's also included in some data mining environments: RapidMiner, PCP, and LIONsolver.

It's also included in some data mining environments: RapidMiner, PCP, and LIONsolver. Automatic model selection which can generate contour of cross validation accuracy.

The package includes the source code of the library in C++ and Java, and a simple program for scaling training data. A README file with detailed explanation is provided. For MS Windows users, there is a sub-directory in the zip file containing binary executable files. Precompiled Java class archive is also included.

Please read the COPYRIGHT notice before using LIBSVM.



Change Run Clear

Examples of options: -s 0 -c 10 -t 1 -g 1 -r 1 -d 3

Classify a binary data with polynomial kernel (u'v+1)^3 and C = 10

options: -s svm_type : set type of SVM (default 0) 0 -- C-SVC 1 -- nu-SVC 2 -- one-class SVM 3 -- epsilon-SVR 4 -- nu-SVR -t kernel_type : set type of kernel function (default 2) 0 -- linear: u'*v 1 -- polynomial: (gamma*u'*v + coef0)^degree 2 -- radial basis function: exp(-gamma*|u-v|^2) 3 -- sigmoid: tanh(gamma*u'*v + coef0) -d degree : set degree in kernel function (default 3) -g gamma : set gamma in kernel function (default 1/num_features) -r coef0 : set coef0 in kernel function (default 0) -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1) -m cachesize : set cache memory size in MB (default 100) -e epsilon : set tolerance of termination criterion (default 0.001) -h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1) -b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0) -wi weight: set the parameter C of class i to weight*C, for C-SVC (default 1) The k in the -g option means the number of attributes in the input data.

To install this tool, please read the README file in the package. There are Windows, X, and Java versions in the package.



Additional Information (how to cite LIBSVM)

References of LIBSVM:

B. Schölkopf, A. Smola, R. Williamson, and P. L. Bartlett. New support vector algorithms. Neural Computation , 12, 2000, 1207-1245.

, 12, 2000, 1207-1245. B. Schölkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. Neural Computation, 13, 2001, 1443-1471.

Interfaces and extensions to LIBSVM