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Maximal width learning of binary functions

By Martin Anthony and Joel Ratsaby


This paper concerns learning binary-valued functions defined on, and investigates how a particular type of ‘regularity’ of hypotheses can be used to obtain better generalization error bounds. We derive error bounds that depend on the sample width (a notion analogous to that of sample margin for real-valued functions). This motivates learning algorithms that seek to maximize sample width

Topics: QA Mathematics
Publisher: Elsevier
Year: 2010
DOI identifier: 10.1016/j.tcs.2009.09.020
OAI identifier:
Provided by: LSE Research Online
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