Feature Selection with SVM

Hi

I want to follow the same approach as suggested here, with t-testing and forward feature selection:

http://www.mathworks.com/help/stats/examples/selecting-features-for-classifying-high-dimensional-data.html#responsive_offcanvas

I have the following code, though my last line throws an error - how can program this approach with SVM? I do not see a straightforward way using crossval in combination with SVM and cvpartition - or am I wrong?

clear
load std_feature; %data to classify, 84x19900 double matrix
load class; % labels, 84x1 double matrix

holdoutCVP = cvpartition(class,'holdout',30); % perform t-test on 54 training samples

dataTrain = std_feature(holdoutCVP.training,:); %trainings-data
classTrain = class(holdoutCVP.training); %trainings-labels

dataTrainG1 = dataTrain(classTrain==1,:); 
dataTrainG2 = dataTrain(classTrain==0,:);

[h,p,ci,stat] = ttest2(dataTrainG1,dataTrainG2,'Vartype','unequal');

ecdf(p);
xlabel('P value');
ylabel('CDF value');

[~,featureIdxSortbyP] = sort(p,2); % sort the features
testMCE = zeros(1,14);

nfs = 5:5:70; % forward feature selection with SVM

for i = 1:14

     fs = featureIdxSortbyP(1:nfs(i));

     SVMModel = fitcsvm(std_feature(:,fs),class);

     % following line does not work for SVMModel:

   testMCE(i) = crossval(SVMModel,'partition',holdoutCVP)...
         /holdoutCVP.TestSize;

end