文件名称：libsvm-3.17
文件大小：869KB
文件格式：ZIP
更新时间：2016-07-15 13:28:54
支持向量机 libsvm
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.
【文件预览】：
libsvm-3.17
----heart_scale(27KB)
----svm-toy()
--------qt()
--------windows()
--------gtk()
----matlab()
--------svmpredict.c(10KB)
--------Makefile(1KB)
--------make.m(798B)
--------svm_model_matlab.h(201B)
--------svmtrain.c(11KB)
--------libsvmread.c(4KB)
--------svm_model_matlab.c(8KB)
--------README(10KB)
--------libsvmwrite.c(2KB)
----COPYRIGHT(1KB)
----Makefile(732B)
----svm-train.c(9KB)
----windows()
--------libsvmread.mexw64(11KB)
--------svm-predict.exe(123KB)
--------svm-train.exe(152KB)
--------svm-scale.exe(79KB)
--------svmpredict.mexw64(25KB)
--------svm-toy.exe(138KB)
--------libsvm.dll(157KB)
--------libsvmwrite.mexw64(10KB)
--------svmtrain.mexw64(63KB)
----svm-scale.c(8KB)
----tools()
--------checkdata.py(2KB)
--------subset.py(3KB)
--------grid.py(15KB)
--------easy.py(3KB)
--------README(7KB)
----java()
--------Makefile(624B)
--------svm_predict.java(5KB)
--------svm_train.java(8KB)
--------svm_scale.java(9KB)
--------test_applet.html(81B)
--------svm_toy.java(12KB)
--------libsvm()
--------libsvm.jar(51KB)
----python()
--------Makefile(32B)
--------svmutil.py(8KB)
--------svm.py(9KB)
--------README(12KB)
----Makefile.win(1KB)
----svm-predict.c(5KB)
----svm.cpp(63KB)
----svm.h(3KB)
----README(28KB)
----svm.def(477B)
----FAQ.html(72KB)
guide.pdf