Fast linear stationary methods for automatically biased support vector machines

We present a new training algorithm, which is capable of providing fast training for a new automatically biased SVM. We compare our algorithm 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.