function output=SVMer(fullkernel,fulllabels)
  fulllabels=full(fulllabels);

badlabels=(fulllabels==0);
numingoer=sum(fulllabels);
its=floor(sum(badlabels)/numingoer);
badlabels=find(badlabels);
goodlabels=find(fulllabels);
badlabels=badlabels(randperm(length(badlabels)));
labels=[ones(numingoer,1);zeros(numingoer,1)];
output=0;


for i=1:its
  kernel=fullkernel([goodlabels badlabels((1+(its-1)*numingoer):(its*numingoer))],[goodlabels badlabels((1+(its-1)*numingoer):(its*numingoer))]);
[train,test]=crossvalind('holdOut',labels);
svmStruct=svmtrain(kernel(train,:),labels(train),'showplot',false);
classes=svmclassify(svmStruct,kernel(test,:),'showplot',false);
cp=classperf(labels);
classperf(cp,classes,test);
output1=cp.CorrectRate;
output=output+output1;


end
output=output/its;