UPDF AI

Agnostic Online Learning

Shai Ben-David,Dávid Pál,S. Shalev-Shwartz

2009 · DBLP: conf/colt/Ben-DavidPS09
Annual Conference Computational Learning Theory · 214 Citations

TLDR

This work describes several models of non-realizable data and derive upper and lower bounds on the achievable regret, and extends Littlestone's theory to include margin-based hypothesis classes, in which the prediction of each hypothesis is accompanied by a confidence value.