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Learning Expert Knowledge and Theorem Proving

  • Conference paper
GWAI-86 und 2. Österreichische Artificial-Intelligence-Tagung

Part of the book series: Informatik-Fachberichte ((2252,volume 124))

Summary

After a brief historical recall, the paper describes, within the approaches issued from Artificial Intelligence, the different methodologies used in Machine Learning.

It describes then one of the main difficulties we encounter: the present impossiblity to compare the generalization state of two formula.

Several definitions are given, compared and criticized We end up on a conjecture for a possible definition that takes into account this criticism.

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Author information

Authors and Affiliations

  1. Laboratoire de Recherche en Informatique, UA 410 du CNRS, Bâtiment 490, Université Paris-Sud, F - 91405, Orsay, France

    Yves Kodratoff

Authors
  1. Yves Kodratoff
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Editor information

Editors and Affiliations

  1. IBM Deutschland GmbH, Abt. LILOG 3504, Postfach 800880, D-7000, Stuttgart 80, Germany

    Claus-Rainer Rollinger

  2. Institut für Medizinische Kybernetik und Artificial Intelligence, Universität Wien, Freyung 6, A-1010, Wien, Austria

    Werner Horn

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© 1986 Springer-Verlag Berlin Heidelberg

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Kodratoff, Y. (1986). Learning Expert Knowledge and Theorem Proving. In: Rollinger, CR., Horn, W. (eds) GWAI-86 und 2. Österreichische Artificial-Intelligence-Tagung. Informatik-Fachberichte, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-71385-9_17

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  • DOI: https://doi.org/10.1007/978-3-642-71385-9_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-16808-9

  • Online ISBN: 978-3-642-71385-9

  • eBook Packages: Springer Book Archive

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