ex2.m

svm classification and regression code

%X=-10:0.51:10;
%X=X';

X=5+rand(30,1).*20;
Y=sin(X)./X;

global p1
p1=2;
ker='rbf';
[nsv, beta, bias] = svr(X,Y,ker);
svrplot(X,Y,ker,beta,bias)
m=size(X,1);
Xt=X+randn(m,1).*0.05;
Yt = svroutput(X,Xt,ker,beta,bias);