Maximal width learning of binary functions ## AbstractThis paper concerns learning binary-valued functions defined on \mathbb{R}, 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 sample width (a notion analagous to that of sample margin for real-valued functions). This motivates learning algorithms that seek to maximize sample width.
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