Support vector machine' s basic theory is the question of second-class classification, commonly used kernel functions include: polynomial, radial basis, Sigmoid type. For the same set of data to select a different kernel function, can be basically similar to the results of training.

SVM_SteveGunn
.............\binomial.m
.............\centrefig.m
.............\cmap.mat
.............\Contents.m
.............\Examples
.............\........\Classification
.............\........\..............\iris1v23.mat
.............\........\..............\iris2v13.mat
.............\........\..............\iris3v12.mat
.............\........\..............\linsep.mat
.............\........\..............\nlinsep.mat
.............\........\Regression
.............\........\..........\example.mat
.............\........\..........\sinc.mat
.............\........\..........\titanium.mat
.............\nobias.m
.............\Optimiser
.............\.........\Makefile
.............\.........\pr_loqo.c
.............\.........\pr_loqo.h
.............\.........\qp.c
.............\.........\qp.dll
.............\qp.dll
.............\README
.............\README.txt
.............\softmargin.m
.............\svc.m
.............\svcerror.m
.............\svcinfo.m
.............\svcoutput.m
.............\svcplot.m
.............\svdatanorm.m
.............\svkernel.m
.............\svr.asv
.............\svr.m
.............\svrerror.m
.............\svroutput.m
.............\svrplot.m
.............\svtol.m
.............\uiclass.m
.............\uiclass.mat
.............\uiregress.m
.............\uiregress.mat

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