Multi Class SVM

Model and classify training/test data sets into more than 2 classes with SVM.

Support Vector Machines only classify data into two classes. This function removes that restriction by "searching" for the correct class for each row in the test data set.

This code is a clarification and optimization of Anand Mishra's code found here:
http://www.mathworks.com/matlabcentral/fileexchange/33170-multi-class-support-vector-machine

Use only with more than 2 classes, otherwise use svmtrain() directly.

Usage Example:

%% SVM Multiclass Example
% SVM is inherently one vs one classification.
% This is an example of how to implement multiclassification using the
% one vs all approach.
TrainingSet=[ 1 10;2 20;3 30;4 40;5 50;6 66;3 30;4.1 42];
TestSet=[3 34; 1 14; 2.2 25; 6.2 63];
GroupTrain=[1;1;2;2;3;3;2;2];
results = multisvm(TrainingSet, GroupTrain, TestSet);
disp('multi class problem');
disp(results);