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International Conference on Inductive Logic Programming

ILP 2002: Inductive Logic Programming pp 346–349Cite as

Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery

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

Abstract

The goals of this presentation are as follows: - Review some key ideas and developments in inductive logic programming. - Show how these ideas can be used in other learning settings, and in particular for the computational scientific discovery of quantitative laws. - Encourage more research on learning in rich representations, such as relational representations and differential equations, which can be used for modeling a variety of real world problems.

This paper also appears in the Proceedings of the Nineteenth Conference on Machine Learning (ICML- 2002), published by Morgan Kaufmann.

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Author information

Authors and Affiliations

  1. Department of Intelligent Systems, Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Sašo Džeroski

Authors
  1. Sašo Džeroski
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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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Džeroski, S. (2003). Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery. 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_23

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  • DOI: https://doi.org/10.1007/3-540-36468-4_23

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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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