Support Vector Machines can be used for data reduction and classification, it is the original sample space through nonlinear mapping transformation to high-dimensional feature space method

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
...........\SVMClassifier.mat
...........\SVMPlot.m
...........\SVMPlot2.m
...........\SVMTest.m
...........\SVMTrain.m
...........\u_LinearSVC.m
...........\u_PolySVC.m
...........\u_RbfSVC.m

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