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DOGMA: A GA-based relational learner

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Inductive Logic Programming (ILP 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1446))

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Abstract

We describe a GA-based concept learning/theory revision system DOGMA and discuss how it can be applied to relational learning. The search for better theories in DOGMA is guided by a novel fitness function that combines the minimal description length and information gain measures. To show the efficacy of the system we compare it to other learners in two relational domains.

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Author information

Authors and Affiliations

  1. Turku Centre for Computer Science, Lemminkäisenkatu 14 A, SF-20520, Turku, Finland

    Jukka Hekanaho

  2. Department of Computer Science, Åbo Akademi University, Lemminkäisenkatu 14 A, SF-20520, Turku, Finland

    Jukka Hekanaho

Authors
  1. Jukka Hekanaho
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David Page

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© 1998 Springer-Verlag Berlin Heidelberg

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Hekanaho, J. (1998). DOGMA: A GA-based relational learner. In: Page, D. (eds) Inductive Logic Programming. ILP 1998. Lecture Notes in Computer Science, vol 1446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0027324

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  • DOI: https://doi.org/10.1007/BFb0027324

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64738-6

  • Online ISBN: 978-3-540-69059-7

  • eBook Packages: Springer Book Archive

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