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Maximal width learning of binary functions

Anthony, Martin and Ratsaby, Joel (2010) Maximal width learning of binary functions. Theoretical Computer Science, 411 (1). pp. 138-147. ISSN 0304-3975

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Abstract

This paper concerns learning binary-valued functions defined on, 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 analogous to that of sample margin for real-valued functions). This motivates learning algorithms that seek to maximize sample width.

Item Type: Article
Official URL: http://www.elsevier.com/locate/tcs
Additional Information: © 2009 Elsevier
Library of Congress subject classification: Q Science > QA Mathematics
Sets: Departments > Mathematics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 11 Aug 2010 11:15
URL: http://eprints.lse.ac.uk/28573/

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