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Function learning from interpolation

By Martin Anthony and Peter L. Bartlett


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

Topics: QA Mathematics
Publisher: Cambridge University Press
Year: 2000
OAI identifier:
Provided by: LSE Research Online

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