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Monotonic versus non-monotonic language learning

  • Steffen Lange
  • Thomas Zeugmann
Selected Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 659)

Abstract

In the present paper strong-monotonic, monotonie and weak-monotonic reasoning is studied in the context of algorithmic language learning theory from positive as well as from positive and negative data.

Strong-monotonicity describes the requirement to only produce better and better generalizations when more and more data are fed to the inference device. Monotonic learning reflects the eventual interplay between generalization and restriction during the process of inferring a language. However, it is demanded that for any two hypotheses the one output later has to be at least as good as the previously produced one with respect to the language to be learnt. Weakmonotonicity is the analogue of cumulativity in learning theory.

We relate all these notions one to the other as well as to previously studied modes of identification, thereby in particular obtaining a strong hierarchy.

Keywords

Initial Segment Recursive Function Inductive Inference Regular Language Positive Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Steffen Lange
    • 1
  • Thomas Zeugmann
    • 2
  1. 1.FB Mathematik und InformatikTH LeipzigLeipzig
  2. 2.Institut für Theoretische InformatikTH DarmstadtDarmstadt

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