28 lines
1.3 KiB
Text
28 lines
1.3 KiB
Text
LIBSVM is an integrated software for support vector classification, (C-SVC,
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nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation
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(one-class SVM). It supports multi-class classification.
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Since version 2.8, it implements an SMO-type algorithm proposed in this paper:
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R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order
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information for training SVM. Journal of Machine Learning Research 6,
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1889-1918, 2005. You can also find a pseudo code there.
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Our goal is to help users from other fields to easily use SVM as a tool. LIBSVM
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provides a simple interface where users can easily link it with their own
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programs. Main features of LIBSVM include
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* Different SVM formulations
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* Efficient multi-class classification
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* Cross validation for model selection
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* Probability estimates
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* Weighted SVM for unbalanced data
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* Both C++ and Java sources
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* GUI demonstrating SVM classification and regression
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* Python, R (also Splus), MATLAB, Perl, Ruby, Weka, Common LISP and LabVIEW
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interfaces. C# .NET code is available.
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It's also included in some learning environments: YALE and PCP.
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* Automatic model selection which can generate contour of cross valiation
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accuracy.
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WWW: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
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Author: Chih-Chung Chang and Chih-Jen Lin <cjlin@csie.ntu.edu.tw>
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