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.

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