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Large width nearest prototype classification on general distance spaces

Anthony, Martin and Ratsaby, Joel (2018) Large width nearest prototype classification on general distance spaces. Theoretical Computer Science, 738 (22). pp. 65-79. ISSN 0304-3975

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Identification Number: 10.1016/j.tcs.2018.04.045


In this paper we consider the problem of learning nearest-prototype classifiers in any finite distance space; that is, in any finite set equipped with a distance function. An important advantage of a distance space over a metric space is that the triangle inequality need not be satisfied, which makes our results potentially very useful in practice. We consider a family of binary classifiers for learning nearest-prototype classification on distance spaces, building on the concept of large-width learning which we introduced and studied in earlier works. Nearest-prototype is a more general version of the ubiquitous nearest-neighbor classifier: a prototype may or may not be a sample point. One advantage in the approach taken in this paper is that the error bounds depend on a 'width' parameter, which can be sample-dependent and thereby yield a tighter bound.

Item Type: Article
Official URL:
Additional Information: © 2018 Elsevier B.V.
Divisions: Mathematics
Subjects: Q Science > QA Mathematics
Date Deposited: 27 Apr 2018 12:13
Last Modified: 29 Jun 2024 01:33
Funders: Suntory and Toyota International Centres for Economics and Related Disciplines

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