Abstract
Previous papers have studied learning of Stochastic Logic Programs (SLPs) either as a purely parametric estimation problem or separated structure learning and parameter estimation into separate phases. In this paper we consider ways in which both the structure and the parameters of an SLP can be learned simultaneously. The paper assumes an ILP algorithm, such as Progol or FOIL, in which clauses are constructed independently. We derive analytical and numerical methods for efficient computation of the optimal probability parameters for a single clause choice within such a search.
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References
J. Cussens. Loglinear models for first-order probabilistic reasoning. In Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence, pages 126–133, San Francisco, 1999. Kaufmann.
J. Cussens. Parameter estimation in stochastic logic programs. Machine Learning, 2000. In press.
A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B, 39:1–38, 1977.
N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In IJCAI-99: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 1300–1309, San Mateo, CA:, 1999. Morgan-Kaufmann.
D. Koller and A. Pfeffer. Learning probabilities for noisy first-order rules. In IJCAI-97: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pages 1316–1321, San Mateo, CA:, 1997. Morgan-Kaufmann.
S. Muggleton. Inverse entailment and Progol. New Generation Computing, 13:245–286, 1995.
S. Muggleton. Inductive logic programming: issues, results and the LLL challenge. Artificial Intelligence, 114(1–2):283–296, December 1999.
S. Muggleton. Learning stochastic logic programs. Electronic Transactions in Artificial Intelligence, 5(041), 2000.
S. Muggleton. Learning from positive data. Machine Learning, 2001. Accepted subject to revision.
S. Muggleton and C. Feng. Efficient induction of logic programs. In Proceedings of the First Conference on Algorithmic Learning Theory, pages 368–381, Tokyo, 1990. Ohmsha.
S. H. Muggleton. Stochastic logic programs. In L. de Raedt, editor, Advances in Inductive Logic Programming, pages 254–264. IOS Press, 1996.
S. H. Muggleton. Learning stochastic logic programs. In Lise Getoor and David Jensen, editors, Proceedings of the AAAI2000 workshop on Learning Statistical Models from Relational Data. AAAI, 2000.
G. Plotkin. A further note on inductive generalization. In Machine Intelligence, volume 6. Edinburgh University Press, 1971.
J. R. Quinlan. Learning logical definitions from relations. Machine Learning, 5:239–266, 1990.
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© 2003 Springer-Verlag Berlin Heidelberg
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Muggleton, S. (2003). Learning Structure and Parameters of Stochastic Logic Programs. 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_13
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DOI: https://doi.org/10.1007/3-540-36468-4_13
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