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Strongly typed inductive concept learning

  • P. A. Flach
  • C. Giraud-Carrier
  • J. W. Lloyd
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1446)

Abstract

In this paper we argue that the use of a language with a type system, together with higher-order facilities and functions, provides a suitable basis for knowledge representation in inductive concept learning and, in particular, illu minates the relationship between attribute-value learning and inductive logic programming (ILP). Individuals are represented by closed terms: tuples of constants in the case of attribute-value learning; arbitrarily complex terms in the case of ILP. To illustrate the point, we take some learning tasks from the machine learning and ILP literature and represent them in Escher, a typed, higher-order, functional logic programming language being developed at the University of Bristol. We argue that the use of a type system provides better ways to discard meaningless hypotheses on syntactic grounds and encompasses many ad hoc approaches to declarative bias.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • P. A. Flach
    • 1
  • C. Giraud-Carrier
    • 1
  • J. W. Lloyd
    • 1
  1. 1.Department of Computer ScienceUniversity of BristolBristolUK

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