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上传时间: 2014-05-20
详细说明： Latest Version （Version 3.18 released on April Fools' day, 2014） 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. ...展开收缩
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