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Function Learning from Interpolation

Anthony, Martin; Bartlett, Peter


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...[Show more]

CollectionsANU Research Publications
Date published: 2000
Type: Journal article
Source: Combinatorics Probability and Computing


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