Generalization error bounds for the logical analysis of data

Anthony, Martin ORCID: 0000-0002-7796-6044 (2012) Generalization error bounds for the logical analysis of data. Discrete Applied Mathematics, 160 (10-11). pp. 1407-1415. ISSN 0166-218X

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Abstract

This paper analyzes the predictive performance of standard techniques for the 'logical analysis of data' (LAD), within a probabilistic framework. We bound the generalization error of classifiers produced by standard LAD methods in terms of their complexity and how well they fit the training data. We also quantify the predictive accuracy in terms of the extent to which the underlying LAD discriminant function achieves a large separation (a 'large margin') between (most of) the positive and negative observations

Item Type:	Article
Official URL:	http://www.journals.elsevier.com/discrete-applied-...
Additional Information:	© 2011 Elsevier B.V.
Divisions:	Mathematics
Subjects:	Q Science > QA Mathematics
Date Deposited:	18 Jan 2012 10:28
Last Modified:	01 Nov 2024 04:21
URI:	http://eprints.lse.ac.uk/id/eprint/41567

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