Learning from a Population of Hypotheses

Michael Kearns, Hyunjune Sebastian Seung

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approximate an unknown target function arbitrarily well. Our motivation includes the question of how to make optimal use of multiple independent runs of a mediocre learning algorithm, as well as settings in which the many hypotheses are obtained by a distributed population of identical learning agents.

Original languageEnglish (US)
Pages (from-to)255-276
Number of pages22
JournalMachine Learning
Volume18
Issue number2
DOIs
StatePublished - Feb 1995

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Keywords

  • PAC learning
  • computational learning theory
  • learning agents
  • machine learning

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