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.

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
  * Weighted SVM for unbalanced data
  * Both C++ and Java sources
  * GUI demonstrating SVM classification and regression
  * Python, R (also Splus), MATLAB, Perl, Ruby, Weka, Common LISP and LabVIEW
    interfaces. C# .NET code is available.
    It's also included in some learning environments: YALE and PCP.
  * Automatic model selection which can generate contour of cross valiation
    accuracy.

Author: Chih-Chung Chang and Chih-Jen Lin <cjlin@csie.ntu.edu.tw>
WWW: http://www.csie.ntu.edu.tw/~cjlin/libsvm/