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Abstract: Support Vector machines find 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 ... (Update)
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BibTeX entry: (Update)
@misc{ cristianini-multiplicative,
author = "Nello Cristianini and Colin Campbell and John Shawe-Taylor",
title = "Multiplicative Updatings for Support-Vector Learning",
url = "citeseer.ist.psu.edu/article/cristianini98multiplicative.html" }
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