Fast linear stationary methods for Automatically Biased Support Vector Machines

Abstract

We present a new training algorithm, which is capable of providing fast training for a new automatically biased SVM. We compare our agorithm to the well-known Sequential Minimal Optimization (SMO) algorithm. We then show that this method allows for the application of acceleration methods which further increases the rates of convergence.