原文地址：学习支持向量机SVM及其代码作者：千里8848
track变成非线性模型。
%主函数
clear all;
close all;
C = 10;
kertype = 'linear';
%训练样本
n = 50;
randn('state',6);
x1 = randn(2,n); %2行N列矩阵
y1 = ones(1,n); %1*N个1
x2 = 5+randn(2,n); %2*N矩阵
y2 = -ones(1,n); %1*N个-1
figure;
plot(x1(1,:),x1(2,:),'bx',x2(1,:),x2(2,:),'k.');
axis([-3 8 -3 8]);
hold on;
X = [x1,x2]; %训练样本d*n矩阵，n为样本个数，d为特征向量个数
Y = [y1,y2]; %训练目标1*n矩阵，n为样本个数，值为+1或-1
svm = svmTrain(X,Y,kertype,C);
plot(svm.Xsv(1,:),svm.Xsv(2,:),'ro');
%测试
[x1,x2] = meshgrid(-2:0.05:7,-2:0.05:7); %x1和x2都是181*181的矩阵
[rows,cols] = size(x1);
nt = rows*cols;
Xt = [reshape(x1,1,nt);reshape(x2,1,nt)];
Yt = ones(1,nt);
result = svmTest(svm, Xt, Yt, kertype);
Yd = reshape(result.Y,rows,cols);
contour(x1,x2,Yd,'m');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function svm = svmTrain(X,Y,kertype,C)
options = optimset; % Options是用来控制算法的选项参数的向量
options.LargeScale = 'off';
options.Display = 'off';
n = length(Y);
H = (Y'*Y).*kernel(X,X,kertype);
f = -ones(n,1); %f为1*n个-1,f相当于Quadprog函数中的c
A = [];
b = [];
Aeq = Y; %相当于Quadprog函数中的A1,b1
beq = 0;
lb = zeros(n,1); %相当于Quadprog函数中的LB，UB
ub = C*ones(n,1);
a0 = zeros(n,1); % a0是解的初始近似值
[a,fval,eXitflag,output,lambda] = quadprog(H,f,A,b,Aeq,beq,lb,ub,a0,options);
epsilon = 1e-8;
sv_label = find(abs(a)>epsilon); %0<a<a(max)则认为x为支持向量
svm.a = a(sv_label);
svm.Xsv = X(:,sv_label);
svm.Ysv = Y(sv_label);
svm.svnum = length(sv_label);
%svm.label = sv_label;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function result = svmTest(svm, Xt, Yt, kertype)
temp = (svm.a'.*svm.Ysv)*kernel(svm.Xsv,svm.Xsv,kertype);
total_b = svm.Ysv-temp;
b = mean(total_b);
w = (svm.a'.*svm.Ysv)*kernel(svm.Xsv,Xt,kertype);
result.score = w + b;
Y = sign(w+b);
result.Y = Y;
result.accuracy = size(find(Y==Yt))/size(Yt);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function K = kernel(X,Y,type)
%X 维数*个数
switch type
case 'linear'
case 'rbf'
end