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Function learning from interpolation

Anthony, Martin and Bartlett, Peter L. (2000) Function learning from interpolation. Combinatorics, Probability and Computing, 9 (3). pp. 213-225. ISSN 0963-5483

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Abstract

In this paper, we study a statistical property of classes of real-valued functions that we call approximation from interpolated examples. We derive a characterization of function classes that have this property, in terms of their ‘fat-shattering function’, a notion that has proved useful in computational learning theory. The property is central to a problem of learning real-valued functions from random examples in which we require satisfactory performance from every algorithm that returns a function which approximately interpolates the training examples.

Item Type: Article
Official URL: http://journals.cambridge.org/action/displayJourna...
Additional Information: © 2000 Cambridge University Press
Library of Congress subject classification: Q Science > QA Mathematics
Sets: Departments > Mathematics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 20 Nov 2008 10:26
URL: http://eprints.lse.ac.uk/7623/

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