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Maximal width learning of binary functions

By Martin Anthony and Joel Ratsaby

Abstract

This paper concerns learning binary-valued functions defined on IR, and investigates how a particular type of ‘regularity’ of hypotheses can be used to obtain better generalization error bounds. We derive error bounds that depend on the sample width (a notion similar to that of sample margin for real-valued functions). This motivates learning algorithms that seek to maximize sample width

Topics: QA Mathematics
Publisher: Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science
Year: 2006
OAI identifier: oai:eprints.lse.ac.uk:13809
Provided by: LSE Research Online
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