C/C++ Language

1. SVMlight

SVMlight  , by Joachims, is one of the most widely used SVM classification and regression packages. It has a fast optimization algorithm, can be applied to very large datasets, and has a very efficient implementation of the leave-one-out cross-validation. Distributed as C++ source and binaries for Linux, Windows, Cygwin, and Solaris. Kernels: polynomial, radial basis function, and neural (tanh).

2. mySVM

mySVM by Stefan Rüping, is a C++ implementation of SVM classification and regression. Available as C++ source code and Windows binaries.

3. GPDT

GPDT, by Serafini, Zanni, and Zanghirati, is a C++ implementation for large-scale SVM classification in both scalar and distributed memory parallel environments. Available as C++ source code and Windows binaries.

4. HeroSvm

HeroSvm, by Dong, is developed in C++, implements SVM classification, and is distributed as a dynamic link library for Windows. Kernels: linear, polynomial, radial basis function.

Java Language

1. Rapidminer

Rapidminer is a Java version of mySVM is part of the YaLE (Yet Another Learning Environment) learning environment. It is the leader in open source provider for data mining and business analytics. The community edition is open source which can be downloaded from website.RapidMiner Server lets to run processes on enterprise hardware from anywhere, without restrictions.

2. LIBSVM

LIBSVM (Library for Support Vector Machines), is developed by Chang and Lin and contains C-classification, ν-classification, ε-regression, and ν-regression. Developed in C++ and Java, it supports also multi-class classification, weighted SVM for unbalanced data, cross-validation and automatic model selection. It supports Java interface.

3. Weka

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

Python Language

1. Scikit Learn

Scikit Learn wraps both liblinear and libsvm. The wrapper was fined-tuned to minimize the memory allocations and impedance mismatch between the python-land numpy.ndarray and scipy.sparse matrix representations  and the internal libsvm representation. Both the dense and sparse variants of libsvm are wrapped.

2. SVMstruct Python

SVMstruct Python is a  python interface to the SVMstruct API for implementing your own structured prediction method. The Python interface makes prototyping much easier and faster than working in C.

3. LIBSVM

LIBSVM (Library for Support Vector Machines), is developed by Chang and Lin and contains C-classification, ν-classification, ε-regression, and ν-regression. Developed in C++ and Java, it supports also multi-class classification, weighted SVM for unbalanced data, cross-validation and automatic model selection. It supports Python interface.

Matlab

1. LIBSVM

LIBSVM (Library for Support Vector Machines), is developed by Chang and Lin and contains C-classification, ν-classification, ε-regression, and ν-regression. Developed in C++ and Java, it supports also multi-class classification, weighted SVM for unbalanced data, cross-validation and automatic model selection. It supports MATLAB interface.

2. Spider

Spider is an object orientated environment for machine learning in MATLAB, for unsupervised, supervised or semi-supervised machine learning problems, and includes training, testing, model selection, cross-validation, and statistical tests. Implements SVM multi-class classification and regression.

3. SVMstruct  Matlab

SVMstruct  Matlab: A matlab interface to the SVMstruct API for implementing your own structured prediction method. Again, prototyping should be much easier and faster than working in C.

This list was compiled by Demnag.