一些支持向量机（SVM）的开源代码库的链接及其简介

（1）LIBSVM:     http://www.csie.ntu.edu.tw/~cjlin/libsvm/

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.

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)

Our goal is to help users from other fields to easily use SVM as a tool. 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, 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 valiation accuracy.

(2)LIBLINEAR   http://www.csie.ntu.edu.tw/~cjlin/liblinear/

LIBLINEAR is a linear classifier for data with millions of instances and features. It supports

• L2-regularized classifiers 
  L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)
• L1-regularized classifiers (after version 1.4) 
  L2-loss linear SVM and logistic regression (LR)
• L2-regularized support vector regression (after version 1.9) 
  L2-loss linear SVR and L1-loss linear SVR.

Main features of LIBLINEAR include

• Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage
• Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
• Cross validation for model selection
• Probability estimates (logistic regression only)
• Weights for unbalanced data
• MATLAB/Octave, Java, Python, Ruby interfaces

(3)SVMlight   http://www.cs.cornell.edu/People/tj/svm_light/

SVM^light is an implementation of Vapnik's Support Vector Machine [Vapnik, 1995] for the problem of pattern recognition, for the problem of regression, and for the problem of learning a ranking function. The optimization algorithms used in SVM^light