Fast Training of Support Vector Classifiers
Fernando Pérez‐Cruz, Pedro Luis Alarcón-Diana, A. Navia-Vázquez, Antonio Artés-Rodrı́guez · 2000
In this communication we present a new algorithm for solving Support Vector Classifiers (SVC) with large training data sets. The new algorithm is based on an Iterative Re-Weighted Least Squares procedure which is used to optimize the SVC. Moreover, a novel sample selection strategy for the working set is presented, which randomly chooses the working set among the training samples that do not fulfill the stopping criteria.