A unifying approach to monotonic language learning on informant

  • Steffen Lange
  • Thomas Zeugmann
Submitted Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 642)

Abstract

The present paper deals with strong-monotonic, monotonic and weak-monotonic language learning from positive and negative examples. The three notions of monotonicity reflect different formalizations of the requirement that the learner has to produce always better and better generalizations when fed more and more data on the concept to be learnt.

We characterize strong-monotonic, monotonic, weak-monotonic and finite language learning from positive and negative data in terms of recursively generable finite sets. Thereby, we elaborate a unifying approach to monotonic language learning by showing that there is exactly one learning algorithm which can perform any monotonic inference task.

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Copyright information

© Springer-Verlag 1992

Authors and Affiliations

  • Steffen Lange
    • 1
  • Thomas Zeugmann
    • 2
  1. 1.FB Mathematik und Informatik PF 66TH LeipzigLeipzig
  2. 2.TH DarmstadtInstitut für Theoretische InformatikDarmstadt

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