function [alpha,b,svs]=svm(K,Y,lambda)% SVM Support Vector Machines Training Routine% [alpha,b,svs]=svm(K,Y,Kernel,KernelParam,C)% % Inputs: % K : A gram matrix% Y corresponding labels [-1,+1] column vector% Kernel = 'linear' | 'poly' | 'rbf'% KernelParam = 0 | degree | gamma% C : SVM C parameter%% Outputs:%% alpha : expansion coefficients (column vector)% b     : bias term% svs   : indices to support vectors (not support vectors themselves !)%%  Author:  Vikas Sindhwani vikass@cs.uchicago.edu%          SSlearn : Semi Supervised Learning Toolbox%          May 2004%------------------------------------------------------------------------------%C=1/(2*lambda);parameters =  [4 1 1 0 C 40.00 0.001 0 0.5 0.1 1] ;% map Y : 1--> 1 0-->2[alpha, svs, b, nsv, nlab] = mexGramSVMTrain(K', Y', parameters);alpha=alpha';% libsvm does some weird label switchingif nlab(1)==-1    alpha=-alpha;enda=zeros(length(Y),1);a(svs+1)=alpha;alpha=a;