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International Workshop on Analogical and Inductive Inference

AII 1992: Analogical and Inductive Inference pp 244–259Cite as

A unifying approach to monotonic language learning on informant

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Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

This research has been supported by the German Ministry for Research and Technology (BMFT) under grant no. 01 IW 101.

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Author information

Authors and Affiliations

  1. FB Mathematik und Informatik PF 66, TH Leipzig, O-7030, Leipzig

    Steffen Lange

  2. TH Darmstadt, Institut für Theoretische Informatik, Alexanderstr. 10, W-6100, Darmstadt

    Thomas Zeugmann

Authors
  1. Steffen Lange
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  2. Thomas Zeugmann
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Editor information

Klaus P. Jantke

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© 1992 Springer-Verlag Berlin Heidelberg

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Lange, S., Zeugmann, T. (1992). A unifying approach to monotonic language learning on informant. In: Jantke, K.P. (eds) Analogical and Inductive Inference. AII 1992. Lecture Notes in Computer Science, vol 642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56004-1_17

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  • DOI: https://doi.org/10.1007/3-540-56004-1_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56004-3

  • Online ISBN: 978-3-540-47339-8

  • eBook Packages: Springer Book Archive

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