Constant rate approximate maximum margin algorithms

Abstract

We present a new class of Perceptron-like algorithms with margin in which the "effective" learning rate ηeff, denned as the ratio of the learning rate to the length of the weight vector, remains constant. We prove that for ηeff sufficiently small the new algorithms converge in a finite number of steps and show that there exists a limit of the parameters involved in which convergence leads to classification with maximum margin. A soft margin extension for Perceptron-like large margin classifiers is also discussed. © Springer-Verlag Berlin Heidelberg 2006.