LIBSVM is an integrated software library for support vector machines, performing support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM)
Reference: LIBSVM -- A Library for Support Vector Machines
It supports multi-class classification.
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
- Various kernels (including precomputed kernel matrix)
- Weighted SVM for unbalanced data
- Both C++ and Java sources
- GUI demonstrating SVM classification and regression
- Python, R, MATLAB, Perl, Ruby, Weka, Common LISP, CLISP, Haskell, OCaml, LabVIEW, and PHP interfaces. C# .NET code and CUDA extension is available. It's also included in some data mining environments: RapidMiner, PCP, and LIONsolver.
- Automatic model selection which can generate contour of cross validation accuracy.
The package includes the source code of the library in C++ and Java, and a simple program for scaling training data. A README file with detailed explanation is provided. For MS Windows users, there is a subdirectory in the zip file containing binary executable files. Precompiled Java class archive is also included.
