文件名称:libsvm-3.1
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [MacOS] [Matlab] [源码]
- 上传时间：
- 2016-12-28
- 文件大小：
- 1.26mb
- 下载次数：
- 0次
- 提 供 者：
- carl****
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
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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 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-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 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
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 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-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 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
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下载文件列表
libsvm-3.1\COPYRIGHT
..........\FAQ.html
..........\heart_scale
..........\java\libsvm\svm.java
..........\....\......\svm.m4
..........\....\......\svm_model.java
..........\....\......\svm_node.java
..........\....\......\svm_parameter.java
..........\....\......\svm_print_interface.java
..........\....\......\svm_problem.java
..........\....\libsvm.jar
..........\....\Makefile
..........\....\svm_predict.java
..........\....\svm_scale.java
..........\....\svm_toy.java
..........\....\svm_train.java
..........\....\test_applet.html
..........\Makefile
..........\Makefile.win
..........\matlab\heart_scale.mat
..........\......\libsvmread.c
..........\......\libsvmread.mexw32
..........\......\libsvmread.mexw64
..........\......\libsvmwrite.c
..........\......\libsvmwrite.mexw32
..........\......\libsvmwrite.mexw64
..........\......\make.m
..........\......\Makefile
..........\......\README
..........\......\svm.obj
..........\......\SVMcgForClass.m
..........\......\SVMcgForRegress.m
..........\......\svmpredict.c
..........\......\svmpredict.mexw32
..........\......\svmpredict.mexw64
..........\......\svmtrain.c
..........\......\svmtrain.c.bak
..........\......\svmtrain.mexw32
..........\......\svmtrain.mexw64
..........\......\svm_model_matlab.c
..........\......\svm_model_matlab.h
..........\......\svm_model_matlab.obj
..........\......-implement[by faruto]\a_template_flow_usingSVM_class.m
..........\...........................\a_template_flow_usingSVM_regress.m
..........\...........................\ClassResult.m
..........\...........................\ClassResult_test.m
..........\...........................\gaSVMcgForClass.m
..........\...........................\gaSVMcgForRegress.m
..........\...........................\gaSVMcgpForRegress.m
..........\...........................\libsvm参数说明.txt
..........\...........................\myprivate\gatbx[Sheffield]\bs2rv.m
..........\...........................\.........\................\contents.m
..........\...........................\.........\................\crtbase.m
..........\...........................\.........\................\crtbp.m
..........\...........................\.........\................\crtrp.m
..........\...........................\.........\................\migrate.m
..........\...........................\.........\................\mpga.m
..........\...........................\.........\................\mut.m
..........\...........................\.........\................\mutate.m
..........\...........................\.........\................\mutbga.m
..........\...........................\.........\................\.ytest\gaSVM.m
..........\...........................\.........\................\ranking.m
..........\...........................\.........\................\recdis.m
..........\...........................\.........\................\recint.m
..........\...........................\.........\................\reclin.m
..........\...........................\.........\................\recmut.m
..........\...........................\.........\................\recombin.m
..........\...........................\.........\................\reins.m
..........\...........................\.........\................\rep.m
..........\...........................\.........\................\resplot.m
..........\...........................\.........\................\rws.m
..........\...........................\.........\................\scaling.m
..........\...........................\.........\................\select.m
..........\...........................\.........\................\sus.m
..........\...........................\.........\................\xovdp.m
..........\...........................\.........\................\xovdprs.m
..........\...........................\.........\................\xovmp.m
..........\...........................\.........\................\xovsh.m
..........\...........................\.........\................\xovshrs.m
..........\...........................\.........\................\xovsp.m
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