GA_SVM

  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。(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