Learning with continuous experts using drifting games

Indraneel Mukherjee, Robert E. Schapire

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

We consider the problem of learning to predict as well as the best in a group of experts making continuous predictions. We assume the learning algorithm has prior knowledge of the maximum number of mistakes of the best expert. We propose a new master strategy that achieves the best known performance for on-line learning with continuous experts in the mistake bounded model. Our ideas are based on drifting games, a generalization of boosting and on-line learning algorithms. We prove new lower bounds based on the drifting games framework which, though not as tight as previous bounds, have simpler proofs and do not require an enormous number of experts. We also extend previous lower bounds to show that our upper bounds are exactly tight for sufficiently many experts. A surprising consequence of our work is that continuous experts are only as powerful as experts making binary or no prediction in each round.

Original languageEnglish (US)
Pages (from-to)2670-2683
Number of pages14
JournalTheoretical Computer Science
Volume411
Issue number29-30
DOIs
StatePublished - Jun 17 2010

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Drifting game
  • Expert advice
  • On-line learning

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