PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Nonconformity Approach to Model Selection for SVMs
David Hardoon, Zakria Hussain and John Shawe-Taylor
(2009) Technical Report. University College London, London, UK.

Abstract

We investigate the issue of model selection and the use of the nonconformity (strangeness) measure in batch learning. Using the nonconformity measure we propose a new training algorithm that helps avoid the need for Cross-Validation or Leave-One-Out model selection strategies. We provide a new generalisation error bound using the notion of nonconformity to upper bound the loss of each test example and show that our proposed approach is comparable to standard model selection methods, but with theoretical guarantees of success and faster convergence. We demonstrate our novel model selection technique using the Support Vector Machine.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4300
Deposited By:David Hardoon
Deposited On:13 March 2009