Learning first-order probabilistic models with combining rules

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 609-616

  Natarajan, Sriraam ^a   Tadepalli, Prasad ^a   Altendorf, Eric ^a   Dietterich, Thomas G ^a   Fern, Alan ^a   Restificar, Angelo ^a  

^a Oregon State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; MATHEMATICAL MODELS; OPTIMIZATION; PARAMETER ESTIMATION; PROBABILISTIC LOGICS;

EXPECTATION MAXIMIZATION; FIRST-ORDER PROBABILISTIC MODELS; RANDOM VARIABLE; REAL-WORLD TASK;

LEARNING SYSTEMS;

EID: 31844444367     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102428     Document Type: Conference Paper

References (17)

1. Altendorf, E. E., Restificar, A. C., & Dietterich, T. G. (2005). Learning from sparse data by exploiting monotonicity constraints. Proceedings of UAI 05.
2. Binder, J., Koller, D., Russell, S., & Kanazawa, K. (1997). Adaptive Probabilistic networks with hidden variables. Machine Learning, 29, 213-244.
3. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, B.39.
4. Domingos, P., & Richardson, M. (2004). Markov logic: A unifying framework for statistical relational learning. Proceedings of the SRL Workshop in ICML.
5. Dragunov, A. N., Dietterich, T. G., Johnsrude, K., McLaughlin, M., Li, L., & Herlocker, J. L. (2005). Task-tracer: A desktop environment to support multi-tasking knowledge workers. Proceedings of IUI.
6. Getoor, L., Friedman, N., Koller, D., & Pfeffer, A. (2001). Learning probabilistic relational models. Invited contribution to the book Relational Data Mining, S. Dzeroski and N. Lavrac, Eds.
7. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The Elements of Statistical learning. Springer.
8. Heckerman, D., & Breese, J. S. (1994). Causal independence for probability assessment and inference using bayesian networks (Technical Report MSR-TR-94-08). Microsoft Research.
9. Heckerman, D., Meek, C., & Koller, D. (2004). Probabilistic models for relational data (Technical Report MSR-TR-2004-30).
10. Jaeger, M. (1997). Relational Bayesian networks. Proceedings of UAI-97.
11. Kersting, K., & Raedt, L. D. (2001). Adaptive bayesian logic programs. Proceedings of the ILP '01 (pp. 104-117).
12. Kersting, K., & Raedt, L. D. (2002). Basic principles of learning bayesian logic programs (Technical Report 172).
13. Koller, D., & Pfeffer, A. (1997). Learning probabilities for noisy first-order rules. IJCAI (pp. 1316-1323).
14. Milch, B., Marthi, B., & Russell, S. (2004). Blog: Relational modeling with unknown objects. Proceedings of the SRL Workshop in ICML.
15. Muggleton, S. (1996). Stochastic logic programs. Advances in Inductive Logic Programming (pp. 254-264).
16. Ngo, L., & Haddawy, P. (1995). Probabilistic logic programming and Bayesian networks. Proceedings ACSC95.
17. Poole, D. (1993). Probabilistic Horn abduction and bayesian networks. Artificial Intelligence, Volume 64, Numbers 1, pages 81-129.