abstract

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).