Conference Paper

Generalised max entropy classifiers

Abstract

In this paper we propose a generalised maximum-entropy classification framework, in which the empirical expectation of the feature functions is bounded by the lower and upper expectations associated with the lower and upper probabilities associated with a belief measure. This generalised setting permits a more cautious appreciation of the information content of a training set. We analytically derive the KarushKuhn-Tucker conditions for the generalised max-entropy classifier in the case in which a Shannon-like entropy is adopted.

Attached files

Authors

Cuzzolin, Fabio

Oxford Brookes departments

Faculty of Technology, Design and Environment\School of Engineering, Computing and Mathematics

Dates

Year of publication: 2018
Date of RADAR deposit: 2018-09-20

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Related resources

This RADAR resource is the Accepted Manuscript of Generalised max entropy classifiers