Multiplicative Updatings for Support-vector Learning Produced as Part of the Esprit Working Group in Neural and Computational Learning Ii, Neurocolt2 27150
Support Vector machines nd maximal margin hyperplanes in a high dimensional feature space. Theoretical results exist which guarantee a high generalization performance when the margin is large or when the number of support vectors is small. Multiplicative-Updating algorithms are a new tool for perceptron learning whose theoretical properties are well studied. In this work we present a Multiplicative-Updating algorithm for learning Support Vector machines which exploits the particular structure of high-generalization hypotheses, by achieving fast rate of convergence just in those situations where high generalization can be obtained, namely small number of support vectors or large margin.