Change of representation for statistical relational learning

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 2719-2726

  Davis, Jesse ^a   Ong, Irene ^a   Struyf, Jan ^a   Burnside, Elizabeth ^a   Page, David ^a   Costa, Vítor Santos ^b  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSICAL PROBLEMS; CLASSIFICATION TASKS; LEARNING APPROACH; RELATIONAL DATA; RELATIONAL DATABASE; RELATIONAL TABLES; STATISTICAL RELATIONAL LEARNING; TARGET CONCEPT;

ARTIFICIAL INTELLIGENCE; DATABASE SYSTEMS; PATENTS AND INVENTIONS; STATISTICS;

LEARNING ALGORITHMS;

EID: 84880880885     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. ACR. Breast imaging reporting and data system (birads), 2004.
2. D. Connolly. Constructing hidden variables in bayesian networks via conceptual clustering. In Proceedings of the Tenth International Conference on Machine Learning, pages 65-72, 1993.
3. J. Davis, E. Burnside, I.C. Dutra, D. Page, and V. Santos Costa. An integrated approach to learning bayesian networks of rules. In 16th ECML, pages 84-95. Springer, 2005.
4. J. Davis, E. Burnside, I.C. Dutra, D. Page, R. Ramakrishnan, V. Santos Costa, and J. Shavlik. View learning for statistical relational learning: With an application to mammography. In Proceedings of the 19th IJCAI, pages 677-683, Edinburgh, Scotland, 2005.
5. N. Friedman, D. Geiger, and M. Goldszmidt. Bayesian networks classifiers. Machine Learning, 29:131-163, 1997.
6. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In Proceedings of the 16th IJCAI. Stockholm, Sweden, 1999.
7. A. Knobbe, M. de Haas, and A. Siebes. Propositionalisation and aggregates. In Proceeding of the 5th PKDD, pages 277-288, 2001.
8. S. Kok and P. Domingos. Learning the structure of markov logic networks. In Proceedings of the 22nd ICML, pages 441-448. ACM Press, 2005.
9. M. Krogel and S. Wrobel. Transformation-based learning using multirelational aggregation. In Proceedings of the 11th ILP, pages 142-155, London, UK, 2001. Springer-Verlag.
10. N. Landwehr, K. Kersting, and L. De Raedt. nFOIL: Integrating Naive Bayes and FOIL. In Proceeding of AAAI, 2005.
11. N. Lavrac, S. Dzeroski, and M. Grobelnik. Learning non-recursive definitions of relations with LINUS. In Y. Kodratoff, editor, Proceedings of the Fifth European Working Session on Learning, LNAI 482, pages 265-281. Springer-Verlag, 1991.
12. A. McCallum, K. Nigam, J. Rennie, and K. Seymore. Automating the construction of internet portals with machine learning. Information Retrieval Journal, 3:127-163, 2000. www.research.whizbang.com/data.
13. S. Muggleton and W. Buntine. Machine invention of first-order predicates by inverting resolution. In Proceedings of the Fifth International Conference on Machine Learning, pages 339-352. Kaufmann, 1988.
14. S. Muggleton. Inverse entailment and Progol. New Generation Computing, 13:245-286, 1995.
15. A. Popescul and L. Ungar. Cluster-based concept invention for statistical relational learning. In Won Kim, Ron Kohavi, Johannes Gehrke, and William DuMouchel, editors, Proceedings of the 10th SIGKDD, pages 665-670, 2004.
16. A. Popescul, L. Ungar, S. Lawrence, and D. Pennock. Statistical relational learning for document mining. In Proceeding of the 3rd ICDM, pages 275-282, 2003.
17. L. Rendell. Substantial constructive induction using layered information compression: tractable feature formation in search. In IJCAI-85, pages 650-658. Kaufmann, 1985.
18. M. Richardson and P. Domingos. Markov logic networks. To appear in Machine Learning, 2006.
19. A. Srinivasan, S. Muggleton, and M. Bain. Distinguishing exeptions from noise in non-monotonic learning. In Proceedings of the 2nd ILP workshop, 1992.
20. A. Srinivasan. The Aleph Manual, 2001.
21. J. Zelle, R. Mooney, and J. Konvisser. Combining top-down and bottom-up techniques in inductive logic programming. In Proceedings of the 11th ICMl, pages 343-351, 1994.