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.
Preview
Unable to display preview. Download preview PDF.
References
Henrik Boström. Predicate invention and learning from positive examples only. In Proceedings of the 10th European Conference on Machine Learning (ECML-98). Springer, 1998.
Wray Buntine. Learning classification trees. In D.J. Hand, editor, Artificial Intelligence Prontiers in Statistics: AI and Statistics III, chapter 15, pages 182–201. Chapman & Hall, London, 1993.
J. Cussens. Bayesian Inductive Logic Programming with explicit probabilistic bias. Technical Report PRG-TR-24-96, Oxford University Computing Laboratory, 1996.
James Cussens. Part-of-speech tagging using Progol. In Inductive Logic Programming: Proceedings of the 7th International Workshop (ILP-97). LNAI 1297, pages 93–108. Springer, 1997.
Luc Dehaspe. Maximum entropy modeling with clausal constraints. In Inductive Logic Programming: Proceedings of the 7th International Workshop (ILP-97). LNAI 1297, pages 109–124. Springer, 1997.
Joseph Y. Halpern. An analysis of first-order logics of probability. Artificial Intelligence, 46:311–350, 1990.
D. Michie, D.J. Spiegelhalter, and C.C. Taylor. Machine Learning, Neural and Statistical Classification. Ellis Horwood, Hemel Hempstead, 1994.
S. Muggleton. Learning from positive data. In S. Muggleton, editor, Inductive Logic Programming: Proceedings of the 6th International Workshop (ILP-96). LNAI 1314, pages 358–376. Springer, 1996.
Raymond Ng and V.S. Subrahmanian. Probabilistic logic programming. Information and Computation, 101(2):150–201, 1992.
Uroš Pompe and Igor Kononenko. Probabilistic first-order classification. In Inductive Logic Programming: Proceedings of the 7th International Workshop (ILP-97), pages 235–243, 1997.
A.F.M. Smith and G.O. Roberts. Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society B, 55(1):3–23, 1993.
A. Srinivasan. Sampling methods for the analysis of large datasets with ILP. Technical Report PRG-TR-27-97, Oxford University Computing Laboratory, Oxford, 1997.
A. Srinivasan and R.D. King. Feature construction with inductive logic programming: A study of quantitative predictions of biological activity aided by structural attributes. In S. Muggleton, editor, Inductive Logic Programming: Proceedings of the 6th International Workshop (ILP-96). LNAZ 1314, pages 89–104. Springer, 1996.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/BFb0027314
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64738-6
Online ISBN: 978-3-540-69059-7
eBook Packages: Springer Book Archive