Exploiting regularity in learning
- Martin Anthony
This talk concerns learning Boolean-valued functions, and discusses how
'regularity' of hypotheses can be used to obtain better generalization error
bounds. For functions defined on simple domains, we derive error bounds that
depend on the 'sample width' (a notion analagous to that of sample margin
for real-valued functions).