Agnostic Online Learning
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.
