Efficient distribution-free learning of probabilistic concepts

Michael J. Kearns, Robert E. Schapire

Research output: Contribution to journalConference articlepeer-review

54 Scopus citations

Abstract

A model of machine learning in which the concept to be learned may exhibit uncertain or probabilistic behavior is investigated. Such probabilistic concepts (or p-concepts) may arise in situations such as weather prediction, where the measured variables and their accuracy are insufficient to determine the outcome with certainty. It is required that learning algorithms be both efficient and general in the sense that they perform well for a wide class of p-concepts and for any distribution over the domain. Many efficient algorithms for learning natural classes of p-concepts are given, and an underlying theory of learning p-concepts is developed in detail.

Original languageEnglish (US)
Pages (from-to)382-391
Number of pages10
JournalIEEE Transactions on Industry Applications
Volume27
Issue number1 pt 1
StatePublished - Jan 1 1991
Externally publishedYes
Event1989 Industry Applications Society Annual Meeting - San Diego, CA, USA
Duration: Oct 1 1989Oct 5 1989

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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