A curated list of awesome Support Vector Machines(SVM) frameworks, libraries and softwares. If you want to contribute to this list (please do), send me a pull request. Also, a listed repository should be deprecated if:
- M-SVM - Multi-class SVM implementation in C by Guermeur.
- Gist - Gist is a C implementation of support vector machine classification and kernel principal components analysis.
- SVMsequel - SVM multi-class classification package, distributed as binaries for Linux or Solaris. Kernels: linear, polynomial, radial basis function, sigmoid, string, tree, information diffusion on discrete manifolds.
- SVM-Light - Distributed as C++ source and binaries for Linux, Windows, Cygwin, and Solaris. Kernels: polynomial, radial basis function, and neural (tanh).
- SVM-Struct - An SVM implementation that can model complex (multivariate) output data y, such as trees, sequences, or sets. Several implementations exist: SVMmulticlass, for multi-class classification; SVMcfg, learns a weighted context free grammar from examples; SVMalign, learns to align protein sequences from training alignments; SVMhmm, learns a Markov model from examples.
- my-SVM - An implementation of SVM classification and regression. It includes the following kernels : linear, polynomial, radial basis function, neural (tanh), anova.
- SVM-Torch - It is part of the Torch machine learning library and implements SVM classification and regression.
- GPDT - An implementation for large-scale SVM classification in both scalar and distributed memory parallel environments. Also available as Windows binaries.
- HeroSvm - Implements SVM classification, and is distributed as a dynamic link library for Windows. Kernels: linear, polynomial, radial basis function.
- SvmFu - A package for SVM classification. Kernels: linear, polynomial, and Gaussian radial basis function.
- TinySVM - An implementation of C-classification and C-regression which uses sparse vector representation.
- Tree Kernels- An extension of SVMlight, obtained by encoding tree kernels. Available as binaries for Windows, Linux, Mac-OSx, and Solaris.
- MATLAB SVM Toolbox - It implements SVM classification and regression with various kernels: linear, polynomial, Gaussian radial basis function, exponential radial basis function, neural (tanh), Fourier series, spline, and B spline.
- Spider - Spider is an object-oriented environment for machine learning in MATLAb that implements SVM multi-class classification and regression.
- LearnSC - MATLAB scripts for the book Learning and Soft Computing by Kecman, implementing SVM classification and regression.
- LS-SVMLab - An implementation of least squares support vector machines (LS-SVM) which reformulates the standard SVM leading to solving linear KKT systems.
- SVM Toolbox - This implementation can handle 1-norm or 2-norm SVM.
- SVM LOO - An efficient implementation of the leave-one-out cross-validation.
- LSVM - An SVM implementation that can classify datasets with several millions patterns.
- ASVM - A linear script developed for large datasets.
- PSVM - A script that classifies patterns by assigning them to the closest of two parallel planes.
- OSU SVM Classifier MATLAB Toolbox - A toolbox based on LibsSVM.
- SimpleSVM Toolbox - An implementation of the SimpleSVM algorithm.
- SVM Toolbox - A fairly complex MATLAB toolbox, containing many algorithms: classification using linear and quadratic penalization, multi-class classification, ε-regression, ν-regression, wavelet kernel, SVM feature selection.
- MATLAB SVM Toolbox - It has standard SVM features, together with multi-class classification and leave-one-out cross-validation.