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International Conference on Inductive Logic Programming

ILP 2002: Inductive Logic Programming pp 317–332Cite as

Noise-Resistant Incremental Relational Learning Using Possible Worlds

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

Abstract

Incremental learning from noisy data is a dificult task and has received very little attention in the field of Inductive Logic Programming. This paper outlines an approach to noisy incremental learning based on a possible worlds model and its implementation in NILE. Several issues relating to the use of this model are addressed. Empirical results are shown for an existing batch domain and also for an interactive learning task.

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Author information

Authors and Affiliations

  1. The University of New South Wales, Sydney, Australia

    James Westendorp

Authors
  1. James Westendorp
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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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Westendorp, J. (2003). Noise-Resistant Incremental Relational Learning Using Possible Worlds. 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_21

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  • DOI: https://doi.org/10.1007/3-540-36468-4_21

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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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