May 26, 2018
Library for Support Vector Machines
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.