For small sample case, SVM simulation results than the neural network is good, but the performance of SVM depends on its two training parameters, the algorithm is automatically selected GA parameters of SVM-2.

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
......\osu_svm3.00\demo
......\osu_svm3.00
GA_SVM

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