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| -rw-r--r-- | science/libsvm/pkg-descr | 35 |
1 files changed, 22 insertions, 13 deletions
diff --git a/science/libsvm/pkg-descr b/science/libsvm/pkg-descr index 95d31e4c9d00..72d16dab601e 100644 --- a/science/libsvm/pkg-descr +++ b/science/libsvm/pkg-descr @@ -1,19 +1,28 @@ 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. The basic algorithm -is a simplification of both SMO by Platt and SVMLight by Joachims. It is also -a simplification of the modification 2 of SMO by Keerthi et al. +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 +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. (how to cite LIBSVM) -Different SVM formulations -Efficient multi-class classification -Cross validation for model selection -Weighted SVM for unbalanced data -Both C++ and Java sources -GUI demonstrating SVM classification and regression +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. WWW: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Author: Chih-Chung Chang and Chih-Jen Lin <cjlin@csie.ntu.edu.tw> |