支持向量机matlab 分类实例及理论 线性支持向量机可对线性可分的样本群进行分类，此时不需要借助于核函数就可较为理想地解决问题。非线性支持向量机将低维的非线性分类问题转化为高维的线性分类问题，然后采用线性支持向量机的求解方法求解。此时需要借助于核函数，避免线性分类问题转化为非线性分类问题时出现的维数爆炸难题，从而避免由于维数太多而无法进行求解。 第 O 层：Matlab 的SVM 函数求解分类问题实例 0.1 Linear classification %Two Dimension Linear-SVM Problem, Two Class and Separable Situation %Method from Christopher J. C. Burges: %"A Tutorial on Support Vector Machines for Pattern Recognition", page 9 %Optimizing ||W|| directly: % Objective: min "f(A)=||W||" , p8/line26 % Subject to: yi*(xi*W+b)-1>=0, function (12); clear all; close all clc; sp=[3,7; 6,6; 4,6;5,6.5] % positive sample points nsp=size(sp); sn=[1,2; 3,5;7,3;3,4;6,2.7] % negative sample points nsn=size(sn) sd=[sp;sn] lsd=[true true true true false false false false false] Y = nominal(lsd) figure(1); subplot(1,2,1) plot(sp(1:nsp,1),sp(1:nsp,2),'m+'); hold on plot(sn(1:nsn,1),sn(1:nsn,2),'c*'); subplot(1,2,2) svmStruct = svmtrain(sd,Y,'showplot',true); 0.2 NonLinear classification clear all; close all clc; sp=[3,7; 6,6; 4,6; 5,6.5] % positive sample points nsp=size(sp); sn=[1,2; 3,5; 7,3; 3,4; 6,2.7; 4,3;2,7] % negative sample points nsn=size(sn) sd=[sp;sn] lsd=[true true true true false false false false false false false] Y = nominal(lsd) figure(1); subplot(1,2,1) plot(sp(1:nsp,1),sp(1:nsp,2),'m+'); hold on plot(sn(1:nsn,1),sn(1:nsn,2),'c*'); subplot(1,2,2) % svmStruct = svmtrain(sd,Y,'Kernel_Function','linear', 'showplot',true); svmStruct = svmtrain(sd,Y,'Kernel_Function','quadratic', 'showplot',true); % use the trained svm (svmStruct) to classify the data RD=svmclassify(svmStruct,sd,'showplot',true) % RD is the classification result vector 0.3 Ga...
