Now that you have idea,you should achive it by youself

You can use Support vector machines  for classification

There's a neural network toolbox in Matlab. But I think it is most efficient to export your features to an external file and then use tools such as SVMlight, Weka, etc'.

 Dear Neamah

First checkout this link :

http://www.mathworks.com/help/nnet/gs/classify-patterns-with-a-neural-network.html

Secondly ,the input to a Neural Network is a single vector, you are passing a matrix. Thus matlab thinks that you want to classify a bunch of vectors (let say 20 vectors). So the vector that you get is the output activation for every of these 20 vectors.

The output activation is a value between 0 and 1 (I guess you are using the sigmoid), so this is perfectly normal. Your job is to get a threshold that fits your data best. You can get this threshold with (cross validation) on your training/test data or by just choosing one (0.5?) and see if the results are "good" and modify if needed.

Regards,