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Maximal width learning of binary functions
Martin Anthony and Joel Ratsaby
CDAM research report series
Number LSE-CDAM-2006-11,
2006.
Abstract This paper concerns learning binary-valued functions defined on $\bbr$, 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 {\em sample width} (a notion similar to that of
sample margin for real-valued functions). This motivates learning
algorithms that seek to maximize sample width. [Edit]
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