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Using prior probabilities and density estimation for relational classification

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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

A Bayesian method for incorporating probabilistic background knowledge into ILP is presented. Positive only learning is extended to allow density estimation. Estimated densities and defined prior are combined in Bayes theorem to perform relational classification. An initial application of the technique is made to part-of-speech (POS) tagging. A novel use of Gibbs sampling for POS tagging is given.

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David Page

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© 1998 Springer-Verlag Berlin Heidelberg

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Cussens, J. (1998). Using prior probabilities and density estimation for relational classification. In: Page, D. (eds) Inductive Logic Programming. ILP 1998. Lecture Notes in Computer Science, vol 1446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0027314

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  • DOI: https://doi.org/10.1007/BFb0027314

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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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