Queries and Concept Learning

  • Authors:
  • Dana Angluin

  • Affiliations:
  • Department of Computer Science, Yale University, P.O. Box 2158, New Haven, CT 06520, U.S.A. ANGLUIN@YALE.EDU

  • Venue:
  • Machine Learning
  • Year:
  • 1988

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Abstract

We consider the problem of using queries to learn an unknown concept. Several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries. Examples are given of efficient learning methods using various subsets of these queries for formal domains, including the regular languages, restricted classes of context-free languages, the pattern languages, and restricted types of prepositional formulas. Some general lower bound techniques are given. Equivalence queries are compared with Valiant's criterion of probably approximately correct identification under random sampling.