Centre for Discrete and Applicable Mathematics |
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CDAM Research Report, LSE-CDAM-2006-11October 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.A PDF file (202 kB) with the full contents of this report can be downloaded by clicking here.
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CDAM Research Reports Series Centre for Discrete and Applicable Mathematics London School of Economics Houghton Street London WC2A 2AE, U.K. |
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