The basic idea of SVM method are: the definition of the optimal linear hyperplane, and the search algorithm for optimal linear hyperplane by solving a convex programming problem. Then based on Mercer nuclear expansion theorem, through a nonlinear mapping φ, the sample space is mapped to a high-dimensional and even infinite dimensional feature space (Hilbert space), so that in the feature space can be applied to solve the linear learning machine method, the sample space The highly nonlinear classification and regression problems. svm procedures that support vector machine code.

svm\binomial.m
...\centrefig.m
...\cmap.mat
...\Contents.m
...\newsvm.zip
...\nobias.m
...\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
...\Optimiser\Makefile
...\.........\pr_loqo.c
...\.........\pr_loqo.h
...\.........\qp.c
...\.........\qp.mexw32
...\Examples\Regression\example.mat
...\........\..........\sinc.mat
...\........\..........\titanium.mat
...\........\Classification\iris1v23.mat
...\........\..............\iris2v13.mat
...\........\..............\iris3v12.mat
...\........\..............\linsep.mat
...\........\..............\nlinsep.mat
...\iris1v23.mat
...\qp.mexw32
...\Examples\Regression
...\........\Classification
...\Optimiser
...\Examples
svm

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