Difference between revisions of "LIBSVM -- A Library for Support Vector Machines"
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Since version 2.8, it implements an SMO-type algorithm proposed in this paper: | 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. | + | 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. |
Main features of LIBSVM include | Main features of LIBSVM include | ||
Line 8: | Line 8: | ||
* Efficient multi-class classification | * Efficient multi-class classification | ||
* Cross validation for model selection | * 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 [http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf]. | Its website maintains a list of packages for download and includes a practical guide for beginner to start with [http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf]. |
Latest revision as of 23:38, 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.
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].