# Centre for Discrete and Applicable Mathematics

## CDAM Research Report, LSE-CDAM-2006-11

October 2006

Maximal Width Learning of Binary Functions

Martin Anthony and Joel Ratsaby

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.

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