Online Learning of Approximate Maximum Margin Classifiers with Biases

Abstract

We consider online learning of linear classifiers which approximately maximize the 2-norm margin. Given a linearly separable sequence of instances, typical online learning algorithms such as Perceptron and its variants, map them into an augmented space with an extra dimension, so that those instances are separated by a linear classifier without a constant bias term. However,this mapping might decrease the margin over the instances. In this paper, we propose a modified version of Li and Long's ROMMA that avoids such the mapping and we show that our modified algorithm achieves higher margin than previous online learning algorithms.