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Maximal width learning of binary functions
Martin Anthony and Joel Ratsaby
Theoretical Computer Science
Volume 411,
,
2010.
AbstractThis paper concerns learning binary-valued functions defined on \mathbb{R}, 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 analagous to that of
sample margin for real-valued functions). This motivates learning
algorithms that seek to maximize sample width. [Edit]
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