支持向量机源码，可在 www.csie.ntu.edu.tw/~cjlin/libsvm/ 下载到最新版本，该版本是 2013年4月更新的，3.17 版。压缩包里面有源代码和文档。以下摘自前述网站： Introduction 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, 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 valiation accuracy.

%% 若转载请注明： % faruto and liyang , LIBSVM-farutoUltimateVersion % a toolbox with implements for support vector machines based on libsvm,2009. % Software available at http://www.matlabsky.com % % Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for % support vector machines, 2001. Software available at % http://www.csie.ntu.edu.tw/~cjlin/libsvm 这是faruto 在libsvm基础上给出相应的辅助函数插件，方便用户来选取最佳的参数，包括：归一化函数:scaleForSVM；pca降维预处理函数：pcaForSVM；网格参数寻优函数(分类问题):SVMcgForClass；网格参数寻优函数(回归问题):SVMcgForRegress；利用PSO参数寻优函数(分类问题):psoSVMcgForClass；利用PSO参数寻优函数(回归问题):psoSVMcgForRegress；利用GA参数寻优函数(分类问题):gaSVMcgForClass；利用GA参数寻优函数(回归问题):gaSVMcgForRegress

一个基于LIBSVM的股票价格预测程序，采用随机森林算法对样本进行训练和预测，使用的编程语言为JAVA。

python3 编写的使用libsvm包进行数据分类训练以及预测等，包含了一份训练数据、测试数据以及数据格式转换的代码

32位64位都可以直接使用

台湾林智仁教授所写的SVM的源代码的详细注释，是学习SVM很好的资料

weka和libsvm和wlsvm的jar包下载，可以在weka中集成后两者进行svm分类

libsvm - 支持多类别分类的svm工具箱（matlab）. 它扩展了matlab自带的svm分类器的功能（只能完成2分类），可配合DeepLearnToolbox使用

这个工具能够生成libsvm数据，直观简单，效率高，方便易行。其实只是利用了excel的宏。

这是一个excel宏命令，可以将训练的样本数据转换为libsvm所要求的输入数据格式。由于很多人都问我要，现在将这个资源共享于此。希望对大家有帮助