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Learning Range Restricted Horn Expressions

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Computational Learning Theory (EuroCOLT 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1572))

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Abstract

We study the learnability of first order Horn expressions from equivalence and membership queries. We show that the class of range restricted Horn expressions, where every term in the consequent of every clause appears also in the antecedent of the clause, is learnable. The result holds both for the model where interpretations are examples (learning from interpretations) and the model where clauses are examples (learning from entailment).

The paper utilises a previous result on learning function free Horn expressions. This is done by using techniques for flattening and unflattening of examples and clauses, and a procedure for model finding for range restricted expressions. This procedure can also be used to solve the implication problem for this class.

This work was partly supported by EPSRC Grant GR/M21409.

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Author information

Authors and Affiliations

  1. Division of Informatics, University of Edinburgh, The King’s Buildings, Edinburgh, EH9 3JZ, Scotland

    Roni Khardon

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  1. Roni Khardon
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Editor information

Editors and Affiliations

  1. Lehrstuhl für Informatik II, Universität Dortmund, D-44221, Dortmund, Germany

    Paul Fischer

  2. Fakultät für Mathematik, Ruhr Universität Bochum, D-44780, Bochum, Germany

    Hans Ulrich Simon

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© 1999 Springer-Verlag Berlin Heidelberg

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Khardon, R. (1999). Learning Range Restricted Horn Expressions. In: Fischer, P., Simon, H.U. (eds) Computational Learning Theory. EuroCOLT 1999. Lecture Notes in Computer Science(), vol 1572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49097-3_10

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  • DOI: https://doi.org/10.1007/3-540-49097-3_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65701-9

  • Online ISBN: 978-3-540-49097-5

  • eBook Packages: Springer Book Archive

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