Built a flexible cross-validation framework into shogun

by Heiko Strathmann for Shogun Machine Learning Toolbox (Technical University Berlin / Max Planck Campus Tübingen)

Nearly every learning machine has parameters which have to be determined manually. Shogun currently lacks a model selection framework. Therefore, the goal of this project is to extend shogun to make cross-validation possible. Different strategies, how training data is split up should be available and easy to exchange. Various model selection schemes are integrated (train,validation,test split, n-fold cross validation, leave one out cross validation, etc)