Support Vector Machines
The foundations of Support Vector Machines (SVM) have been developed by Vapnik , and are gaining popularity due to many attractive features,and promising empirical performance. The formulation embodies the Structural Risk Minimisation (SRM) principle, as opposed to the Empirical Risk Minimisation (ERM) approach commonly employed within statistical learning methods. SRM minimises an upper boound on the generalisation error, as opposed to ERM which minmises the error on the training data. It is this difference which equips SVMs with a greater potential to generalise, which is our goal in statistical learning. The SVM can be applied to both classification and regression problems.
Sparse Kernel Modelling