The projectron: A bounded kernel-based perceptron

Francesco Orabona, Joseph Keshet, Barbara Caputo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

100 Scopus citations

Abstract

We present a discriminative online algorithm with a bounded memory growth, which is based on the kernel-based Perception. Generally, the required memory of the kernel-based Perceptron for storing the online hypothesis is not bounded. Previous work has been focused on discarding part of the instances in order to keep the memory bounded. In the proposed algorithm the instances are not discarded, but projected onto the space spanned by the previous online hypothesis. We derive a relative mistake bound and compare our algorithm both analytically and empirically to the state-of-the-art Forgetron algorithm (Dekel et al, 2007). The first variant of our algorithm, called Projectron, outperforms the Forgetron. The second variant, called Projectron++, outperforms even the Perceptron. Copyright 2008 by the author(s)/owner(s).
Original languageEnglish (US)
Title of host publicationProceedings of the 25th International Conference on Machine Learning
Pages720-727
Number of pages8
StatePublished - Nov 26 2008

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