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International Workshop on Algorithmic Learning Theory

ALT 1996: Algorithmic Learning Theory pp 143–155Cite as

Boosting first-order learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1160))

Abstract

Several empirical studies have confirmed that boosting classifier-learning systems can lead to substantial improvements in predictive accuracy. This paper reports early experimental results from applying boosting to ffoil, a first-order system that constructs definitions of functional relations. Although the evidence is less convincing than that for propositional-level learning systems, it suggests that boosting will also prove beneficial for first-order induction.

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Author information

Authors and Affiliations

  1. University of Sydney, 2006, Sydney, N.S.W., Australia

    J. R. Quinlan

Authors
  1. J. R. Quinlan
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Editor information

Setsuo Arikawa Arun K. Sharma

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© 1996 Springer-Verlag Berlin Heidelberg

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Quinlan, J.R. (1996). Boosting first-order learning. In: Arikawa, S., Sharma, A.K. (eds) Algorithmic Learning Theory. ALT 1996. Lecture Notes in Computer Science, vol 1160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61863-5_42

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  • DOI: https://doi.org/10.1007/3-540-61863-5_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61863-8

  • Online ISBN: 978-3-540-70719-6

  • eBook Packages: Springer Book Archive

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