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Neural Networks and Computational Learning Theory

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NeuroCOLT Technical Report NC-TR-96-032

A Framework for Stuctural Risk Minimisation

John Shawe-Taylor
Royal Holloway, University of London
UK

Peter Bartlett
RSISE
Australian National University
Australia

Robert Williamson
Australian National University
Australia

Martin Anthony
London School of Economics
UK

Abstract
The paper introduces a framework for studying structural risk minimisation. The model views structural risk minimisation in a PAC context. It then generalises to the case when the hierarchy of classes is chosen in response to the data, hence explaining the impressive performance of the maximal margin hyperplane algorithm of Vapnik.

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