Learning with continuous experts using drifting games

Indraneel Mukherjee, Robert E. Schapire

Research output: Contribution to journalConference article

1 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 online learning with continuous experts in the mistake bounded model. Our ideas are based on drifting games, a generalization of boosting and online learning algorithms. We also 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.

Original languageEnglish (US)
Pages (from-to)240-255
Number of pages16
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5254 LNAI
DOIs
StatePublished - Dec 10 2008
Event19th International Conference on Algorithmic Learning Theory, ALT 2008 - Budapest, Hungary
Duration: Oct 13 2008Oct 16 2008

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Learning with continuous experts using drifting games'. Together they form a unique fingerprint.

  • Cite this