A new perspective on an old perceptron algorithm

Abstract

We present a generalization of the Perceptron algorithm. The new algorithm performs a Perceptron-sty le update whenever the margin of an example is smaller than a predefined value. We derive worst case mistake bounds for our algorithm. As a byproduct we obtain a new mistake bound for the Perceptron algorithm in the inseparable case. We describe a multiclass extension of the algorithm. This extension is used in an experimental evaluation in which we compare the proposed algorithm to the Perceptron algorithm. © Springer-Verlag Berlin Heidelberg 2005.