Difference between revisions of "LIBSVM -- A Library for Support Vector Machines"
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Main features of LIBSVM include | Main features of LIBSVM include | ||
| − | + | * Different SVM formulations | |
| − | + | * Efficient multi-class classification | |
| − | + | * Cross validation for model selection | |
| − | |||
* Probability estimates | * Probability estimates | ||
* Weighted SVM for unbalanced data | * Weighted SVM for unbalanced data | ||
Revision as of 23:37, 30 September 2010
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. It is actively patched and has interfaces in many different language including Java, Matlab, C# etc. [1]
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)
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, CLISP, Haskell and LabVIEW interfaces. C# .NET code is available.
It's also included in some data mining environments: RapidMiner and PCP.
* Automatic model selection which can generate contour of cross valiation accuracy.
Its website maintains a list of packages for download and includes a practical guide for beginner to start with [2].
