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PAC-Bayes analysis of maximum entropy learning

Shawe-Taylor, J; Hardoon, DR; (2009) PAC-Bayes analysis of maximum entropy learning. In: Journal of Machine Learning Research. (pp. 480 - 487). Gold open access

Abstract

We extend and apply the PAC-Bayes theorem to the analysis of maximum entropy learning by considering maximum entropy classification. The theory introduces a multiple sampling technique that controls an effective margin of the bound. We further develop a dual implementation of the convex optimisation that optimises the bound. This algorithm is tested on some simple datasets and the value of the bound compared with the test error.© 2009 by the authors.

Type: Proceedings paper
Title: PAC-Bayes analysis of maximum entropy learning
Open access status: An open access publication
Publisher version: http://www.jmlr.org/
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/79055
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