On Error and Compression Rates for Prototype Rules. (arXiv:2206.08014v1 [cs.LG])

Web: http://arxiv.org/abs/2206.08014

We study the close interplay between error and compression in the
non-parametric multiclass classification setting in terms of prototype learning
rules. We focus in particular on a close variant of a recently proposed
compression-based learning rule termed OptiNet. Beyond its computational
merits, this rule has been recently shown to be universally consistent in any
metric instance space that admits a universally consistent rule -- the first
learning algorithm known to enjoy this property. However, its error and
compression rates have …

arxiv compression error lg on rules