An accelerated MDM algorithm for SVM training

This is an electronic version of the paper presented at the 16th European Symposium on Artificial Neural Networks, held in Bruges on 2018In this work we will propose an acceleration procedure for the Mitchell–Demyanov–Malozemov (MDM) algorithm (a fast geometric algorithm for SVM construction) that may yield quite large training savings. While decomposition algorithms such as SVMLight or SMO are usually the SVM methods of choice, we shall show that there is a relationship between SMO and MDM that suggests that, at least in their simplest implementations, they should have similar training speeds. Thus, and although we will not discuss it here, the proposed MDM acceleration might be used as a starting point to new ways of accelerating SMO.With partial support of Spain’s TIN 2004–07676 and TIN 2007–66862 projects. The first author is kindly supported by FPU-MEC grant reference AP2006-02285