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.
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
o Different SVM formulations
o Efficient multi-class classification
o Cross validation for model selection
o Probability estimates
o Weighted SVM for unbalanced data
o Both C++ and Java sources
o GUI demonstrating SVM classification and regression
o Python, R (also Splus), MATLAB, Perl, Ruby, Weka, CLISP and LabVIEW interfaces. C# .NET code is available. It's also included in some learning environments: YALE and PCP.
o Automatic model selection which can generate contour of cross valiation accuracy.
- Changes to previous version:
Initial Announcement on mloss.org.
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