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Relational Learning with Transfer of Knowledge Between Domains

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Advances in Artificial Intelligence (Canadian AI 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1822))

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Abstract

A commonly used relational learning system (FOIL) is extended through the use of clichés, which are known to address FOIL’s greedy search deficiencies. The issue of finding good biases in the form of clichés is addressed by learning the clichés. This paper shows empirically that such biases can be learned in one domain and applied in another, and that significant improvement in accuracy can be achieved in this setting. The approach is applied to a real-life problem of learning finite element method structures from examples.

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Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, University of Ottawa, Ontario, K1N 6N5, Canada

    Johanne Morin & Stan Matwin

Authors
  1. Johanne Morin
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  2. Stan Matwin
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, SK, S4S 0A2, Canada

    Howard J. Hamilton

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© 2000 Springer-Verlag Berlin Heidelberg

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Morin, J., Matwin, S. (2000). Relational Learning with Transfer of Knowledge Between Domains. In: Hamilton, H.J. (eds) Advances in Artificial Intelligence. Canadian AI 2000. Lecture Notes in Computer Science(), vol 1822. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45486-1_32

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  • DOI: https://doi.org/10.1007/3-540-45486-1_32

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67557-0

  • Online ISBN: 978-3-540-45486-1

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