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Revision of First-Order Bayesian Classifiers

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Book cover Inductive Logic Programming (ILP 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2583))

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Abstract

New representation languages that integrate first order logic with Bayesian networks have been proposed in the literature. Probabilistic Relational models (PRM) and Bayesian Logic Programs (BLP) are examples. Algorithms to learn both the qualitative and the quantitative components of these languages have been developed. Recently, we have developed an algorithm to revise a BLP. In this paper, we discuss the relationship among these approaches, extend our revision algorithm to return the highest probabilistic scoring BLP and argue that for a classi- fication task our approach, which uses techniques of theory revision and so searches a smaller hypotheses space, can be a more adequate choice.

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Author information

Authors and Affiliations

  1. Programa de Engenharia de Sistemas e ComputaÇão-COPPE, Universidade Federal do Rio de Janeiro, Caixa Postal 68511, 21945-970, Rio de Janeiro RJ, Brasil

    Kate Revoredo & Gerson Zaverucha

Authors
  1. Kate Revoredo
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  2. Gerson Zaverucha
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Editor information

Editors and Affiliations

  1. School of Information Technology and Engineering, University of Ottawa, 800 King Edward Ave., K1N 6N5, Ottawa, ON, Canada

    Stan Matwin

  2. School of Computer Science and Engineering, University of New South Wales, 2052, Sydney, NSW, Australia

    Claude Sammut

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© 2003 Springer-Verlag Berlin Heidelberg

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Revoredo, K., Zaverucha, G. (2003). Revision of First-Order Bayesian Classifiers. In: Matwin, S., Sammut, C. (eds) Inductive Logic Programming. ILP 2002. Lecture Notes in Computer Science(), vol 2583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36468-4_15

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  • DOI: https://doi.org/10.1007/3-540-36468-4_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00567-4

  • Online ISBN: 978-3-540-36468-9

  • eBook Packages: Springer Book Archive

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