I have a set of data composed of list of 35 features. I notice when I give the data to svmtrain I get the message:

no convergence achieved within maximum number of iterations

Than, when I increase the number if iteration " MaxIter " up to around 1,000,000 the above error disappear and I start getting good classification using " svmclassify ".

On the other hand, when I give the data to " fitcsvm " it converge quickly within the default number of iteration "15,000". However, the problem is when I try to classify the data using " predict ", I got wrong classification.

So in a nutshell, at last " svmtrain " classify the data correctly after increasing number of iteration. However," fitcsvm " neither classify the data correctly, nor it gives me the opportunity to increase number of iteration because it looks from checking the ConvergenceInfo.Converged property that it converge successfully.

Any advice please? notice I'm new to matlab and SVM. 

Answer: 

fitcsvm and svmtrain use, among other algorithms, SMO for optimization. The software implements SMO differently between the two functions, but numerical studies show that there is sensible agreement in the results.

Source: http://in.mathworks.com/help/stats/classificationsvm-class.html

Where the SMO is Sequential Minimal Optimization. This is a step from the steps of training the SVM.

Apparently the two implementations of SMO have different parameters to configure. That is why in the fitcsvm's implementation, you don't get to set the number of iterations.

Have a look at the link above, it also mentions the other differences.