文件名称:zhichixiangliangjisuanfa(svm)

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  • 图形图象
  • 资源属性:
  • [Matlab] [源码]
  • 上传时间:
  • 2015-10-28
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  • 125kb
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  • 0次
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SVM支持向量机分类训练算法的matlab实现,对正负样本的分类效果很好.-SVM support vector machine classification training algorithm matlab implementation, the positive and negative samples of the classification effect is very good..
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zhichixiangliangjisuanfa(svm)\zhichixiangliangjisuanfa(svm)\binomial.m

.............................\.............................\centrefig.m

.............................\.............................\cmap.mat

.............................\.............................\Contents.m

.............................\.............................\Examples\Classification\iris1v23.mat

.............................\.............................\........\..............\iris2v13.mat

.............................\.............................\........\..............\iris3v12.mat

.............................\.............................\........\..............\linsep.mat

.............................\.............................\........\..............\nlinsep.mat

.............................\.............................\........\Regression\example.mat

.............................\.............................\........\..........\sinc.mat

.............................\.............................\........\..........\titanium.mat

.............................\.............................\newsvm.zip

.............................\.............................\nobias.m

.............................\.............................\Optimiser\Makefile

.............................\.............................\.........\pr_loqo.c

.............................\.............................\.........\pr_loqo.h

.............................\.............................\.........\qp.c

.............................\.............................\.........\qp.dll

.............................\.............................\qp.dll

.............................\.............................\README

.............................\.............................\softmargin.m

.............................\.............................\svc.m

.............................\.............................\svcerror.m

.............................\.............................\svcinfo.m

.............................\.............................\svcoutput.m

.............................\.............................\svcplot.m

.............................\.............................\svdatanorm.m

.............................\.............................\svkernel.m

.............................\.............................\svr.m

.............................\.............................\svrerror.m

.............................\.............................\svroutput.m

.............................\.............................\svrplot.m

.............................\.............................\svtol.m

.............................\.............................\uiclass.m

.............................\.............................\uiclass.mat

.............................\.............................\uiregress.m

.............................\.............................\uiregress.mat

.............................\.............................\Examples\Classification

.............................\.............................\........\Regression

.............................\.............................\Examples

.............................\.............................\Optimiser

.............................\zhichixiangliangjisuanfa(svm)

zhichixiangliangjisuanfa(svm)

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