Multiplicative Updatings for Support Vector Machines

Support Vector Machines find maximal margin hyperplanes in a high dimensional feature space, represented as a sparse linear combination of training points. Theoretical results exist which guarantee a high generalization performance when the margin is large or when the representation is very sparse. Multiplicative-Updating algorithms are a new tool for perceptron learning which are guaranteed to converge rapidly when the target concept is sparse. In this paper we present a Multiplicative-Updating algorithm for training Support Vector Machines which combines the generalization power provided by VC theory with the convergence properties of multiplicative algorithms.