PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Tracking experts that learn by evolving past posteriors
Wouter Koolen and Tim Erven, van
Proceedins of the 18th Annual Belgian-Dutch Conference on Machine Learning pp. 91-92, 2009.

Abstract

A problem posed by Freund is how to efficiently track a small pool of experts out of a much larger set. This problem was solved when Bousquet andWarmuth introduced their mixing past posteriors (MPP) algorithm in 2001. In Freund’s problem the experts would normally be considered black boxes. However, in this paper we re-examine Freund’s problem in case the experts have internal structure that enables them to learn. In this case the problem has two possible interpretations: should the experts learn from all data or only from the subsequence on which they are being tracked? The MPP algorithm solves the first case. We generalise MPP to address the second option. Our results apply to any expert structure that can be formalised using (expert) hidden Markov models. Curiously enough, for our interpretation there are two natural reference schemes: freezing and sleeping. For each scheme, we provide an efficient prediction strategy

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6007
Deposited By:Wouter Koolen
Deposited On:08 March 2010