Multi-category classifiers and sample width

Anthony, Martin and Ratsaby, Joel (2016) Multi-category classifiers and sample width. Journal of Computer and System Sciences, 82 (8). pp. 1223-1231. ISSN 0022-0000

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Abstract

In a recent paper, the authors introduced the notion of sample width for binary classifier defined on the set of real numbers. It was shown that the performance of such classifier could be quantified in terms of this sample width. This paper considers how to adapt the idea of sample width so that it can be applied in cases where the classifier are multi-category and are defined on some arbitrary metric space.

Item Type:	Article
Official URL:	http://www.journals.elsevier.com/journal-of-comput...
Additional Information:	© 2016 Published by Elsevier Inc.
Divisions:	Mathematics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	29 Apr 2016 11:20
Last Modified:	07 Jan 2024 18:54
Projects:	ST-2007-216886
Funders:	ST Programme of the European Community, Suntory and Toyota International Centres for Economics and Related Disciplines
URI:	http://eprints.lse.ac.uk/id/eprint/66277

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