Learning directed probabilistic logical models: Ordering-search versus structure-search

Annals of Mathematics and Artificial Intelligence

Volumn 54, Issue 1-3, 2008, Pages 99-133

  Fierens, Daan ^a   Ramon, Jan ^a   Bruynooghe, Maurice ^a   Blockeel, Hendrik ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Bayesian networks; Inductive logic programming; Probabilistic logical models; Probability trees; Statistical relational learning; Structure learning

Indexed keywords

EID: 70350008040     PISSN: 10122443     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10472-009-9134-9     Document Type: Article

References (23)

1. Boutilier, C., Friedman, N., Goldszmidt, M., Koller, D.: Context-specific independence in Bayesian networks. In: Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, pp. 115-123, Portland, 1-4 August 1996.
2. Chickering, D.: Learning equivalence classes of Bayesian-network structures. J. Mach. Learn. Res. 2, 445-498 (2002).
3. Cooper, G., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309-347 (1992).
4. Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T.M., Nigam, K., Slattery, S.: Learning to extract symbolic knowledge from the world wide web. In: Proceedings of the 15th National Conference on Artificial Intelligence, pp. 509-516 (1998).
5. De Raedt, L., Kersting, K.: Probabilistic inductive logic programming. In: Proceedings of the 15th International Conference on Algorithmic Learning Theory. Lecture Notes in Computer Science, vol. 3244, pp. 19-36. Springer, New York (2004).
6. Fierens, D., Blockeel, H., Bruynooghe, M., Ramon, J.: Logical Bayesian networks and their relation to other probabilistic logical models. In: Proceedings of the 15th International Conference on Inductive Logic Programming. Lecture Notes in Computer Science, vol. 3625, pp. 121-135. Springer, New York (2005).
7. Fierens, D., Ramon, J., Blockeel, H., Bruynooghe, M.: A comparison of pruning criteria for probability trees. Tech. Rep. CW 488, Department of Computer Science, Katholieke Universiteit Leuven. http://www.cs.kuleuven.ac.be/publicaties/rapporten/cw/CW488.abs.html (2007).
8. Fierens, D., Ramon, J., Bruynooghe, M., Blockeel, H.: Learning directed probabilistic logical models from relational data. Tech. Rep. CW 490, Department of Computer Science, Katholieke Universiteit Leuven. http://www.cs.kuleuven.ac.be/publicaties/rapporten/cw/CW490.abs.html (2007).
9. Fierens, D., Ramon, J., Bruynooghe, M., Blockeel, H.: Learning directed probabilistic logical models: ordering-search versus structure-search. In: Proceedings of 18th European Conference on Machine Learning. Lecture Notes in Artificial Intelligence, vol. 4701, pp. 567-574. Springer, New York (2007).
10. Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, pp. 1300-1307. Morgan Kaufmann, Stockholm (1999).
11. Friedman, N., Goldszmidt, M.: Learning Bayesian networks with local structure. In: Jordan, M. (ed.) Learning in Graphical Models, pp. 421-459. Kluwer Academic, Dordrecht (1998).
12. Getoor, L., Friedman, N., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Dzeroski, S., Lavrac, N. (eds.) Relational Data Mining, pp. 307-334. Springer, New York (2001).
13. Heckerman, D., Geiger, D., Chickering, D.: Learning Bayesian networks: the combination of knowledge and statistical data. Mach. Learn. 20, 197-243 (1995).
14. Jaeger, M.: Parameter learning for relational Bayesian networks. In: Proceedings of the 24th International Conference on Machine Learning, Cincinnati, 13-15 December 2007.
15. Kersting, K., De Raedt, L.: Towards combining inductive logic programming and Bayesian networks. In: Proceedings of the 11th International Conference on Inductive Logic Programming. Lecture Notes in Computer Science, vol. 2157, pp. 118-131. Springer, New York (2001).
16. Kersting, K., De Raedt, L.: Basic principles of learning Bayesian logic programs. Tech. Rep. 174, Institute for Computer Science, University of Freiburg, Germany (2002).
17. Lloyd, J.: Foundations of Logic Programming, 2nd edn. Springer, New York (1987).
18. Mihalkova, L., Huynh, T., Mooney, R.: Mapping and revising Markov logic networks for transfer learning. In: Proceedings of the 22nd Conference on Artificial Intelligence, Vancouver, 22-26 July 2007.
19. Natarajan, S., Wong, W., Tadepalli, P.: Structure refinement in first order conditional influence language. In: Proceedings of the ICML workshop on Open Problems in Statistical Relational Learning, Pittsburgh, 25-29 June 2006.
20. Ramon, J., Croonenborghs, T., Fierens, D., Blockeel, H., Bruynooghe, M.: Generalized ordering-search for learning directed probabilistic logical models. Mach. Learn. 70(2-3), 169-491 (2008).
21. Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1-2), 107-136 (2006).
22. Teyssier, M., Koller, D.: Ordering-based search: a simple and effective algorithm for learning Bayesian networks. In: Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, pp. 584-590. AUAI, Helsinki (2005).
23. Van Assche, A., Vens, C., Blockeel, H., Džeroski, S.: First order random forests: learning relational classifiers with complex aggregates. Mach. Learn. 64(1-3), 149-182 (2006).