An IRWLS procedure for SVR

In this paper we propose an Iterative Re-Weighted Least Square procedure in order to solve the Support Vector Machines for regression and function estimation. Furthermore, we include a new algorithm to train Support Vector Machines, covering both the proposed approach instead of the quadratic programming part and the most advanced methods to deal with large training data sets. Finally, the performance of the method is assessed by selected examples which show that the training time is much shorter and the memory requirements much less than the employed ones by current methods.