SMO算法程序的使用

最近在网上搜了很久SMO算法程序，程序贴在下面了。但不知道怎么使用。特别是怎样运用到支持向量回归机的预测中。请问哪位能指点一下我。另外，网上有很多C++的程序，不知哪能找到相关的matlab程序。万分感谢！！！

SMO程序：

target = desired output vector

point = training point matrix

procedure takeStep(i1,i2)

if(i1==i2) return 0

alph1 = Lagrange multiplier for i1

y1 = target[i1]

E1 = SVM output on point[i1] - y1(check in error cache)

s=y1*y2

Compute L ,H

if(L==H)

return 0

k11 = kernel(point[i1],point[i1])

k12 = kernel(point[i1],point[i2])

k22 = kernel(point[i2],point[i2])

eta = 2*k12-k11-k22

if(eta<0)

{ a2=alph2 - y2*(E1-E2)/eta

if(a2

else if(a2>H) a2=H

}

else

{Lobj=objective function at a2=L

Hobj=objective function at a2=H

if(Lobj>Hobj+eps)

a2=L

else if(Lobj

a2=H

else

a2=alph2

}

if(|a2-alpha2|

return 0

a1=alpha1+s*(alpha2-a2)

Update threshold to reflect change in Lagrange multipliers

Update weight vector to reflect change in a1&a2,if linear SVM

Update error cache using new Lagrange multipliers

Store a1 in the alpha array

Store a2 in the alpha array

return 1

endprocedure

procedure examineExample(i2)

y2=target[i2]

alpha2=Lagrange multiplier for i2

E2=SVM output on point[i2]-y2(check in error cache)

r2=E2*y2

if((r2tol&&alph2>0))

{if(number of non-zero&non-C alpha>1)

{i1=result of second choice heuristic

if takeStep(i1,i2)

return 1

}

loop over all possible i1,starting at random point

{i1=identity of current alpha

if takeStep(i1,i2)

return 1

}

}

return 0

endprocedure

main routine:

initialize alpha array to all zero

initialize threshold to zero

numChange=0

examineAll=1

while(numChange>0|examineAll)

{numChanged =0

if(examineAll)

loop I over all training examples

numChanged+=examineExample(I)

if(examineAll==1)

examineAll=0

else if(numChanged==0)

examineAll=1

}