用GA自动选择SVM的两个参数我要分享

Automatic selection of two parameters of SVM with GA

小样本训练 GASVM ga-neural-network SVM--快速 GA-SVM

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文件大小: 1.60M

代码分类: 智能算法

开发平台: matlab

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代码描述

中文说明:对于小样本而言,SVM的仿真效果要比神经网络好,但是SVM的性能依赖于它的两个训练参数,本算法是用GA自动选择SVM的两个参数。


English Description:

However, the performance of SVM neural network is better than that of SVM neural network.


代码预览

GA_SVM

......\ALLdataTest.m

......\ALLdataTrain.m

......\mainGA7.m

......\osu_svm3.00

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

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

......\...........\demo

......\...........\....\c_clademo.m

......\...........\....\c_lindemo.m

......\...........\....\c_poldemo.m

......\...........\....\c_rbfdemo.m

......\...........\....\c_svcdemo.m

......\...........\....\DemoData_class.mat

......\...........\....\DemoData_test.mat

......\...........\....\DemoData_train.mat

......\...........\....\one_rbfdemo.m

......\...........\....\osusvmdemo.m

......\...........\....\SVMClassifier.mat

......\...........\....\u_clademo.m

......\...........\....\u_lindemo.m

......\...........\....\u_poldemo.m

......\...........\....\u_rbfdemo.m

......\...........\....\u_svcdemo.m

......\...........\demos.m

......\...........\LinearSVC.m

......\...........\mexSVMClass.dll

......\...........\mexSVMClass.m

......\...........\mexSVMClass.mexglx

......\...........\mexSVMClass.mexhp7

......\...........\mexSVMClass.mexsol

......\...........\mexSVMTrain.dll

......\...........\mexSVMTrain.m

......\...........\mexSVMTrain.mexglx

......\...........\mexSVMTrain.mexhp7

......\...........\mexSVMTrain.mexsol

......\...........\Normalize.m

......\...........\one_RbfSVC.m

......\...........\PolySVC.m

......\...........\RbfSVC.m

......\...........\Scale.m

......\...........\SVMClass.m

......\...........\SVMPlot.m

......\...........\SVMPlot2.m

......\...........\SVMTest.m

......\...........\SVMTrain.m

......\...........\u_LinearSVC.m

......\...........\u_PolySVC.m

......\...........\u_RbfSVC.m

......\Readme_of_GASVM.txt

......\selectGA7.m

......\svmc7.m