Learning probabilistic models of link structure

Journal of Machine Learning Research

Volumn 3, Issue 4-5, 2003, Pages 679-707

  Getoor, Lise ^a   Friedman, Nir ^b   Koller, Daphne ^c   Taskar, Benjamin ^c  

^a UNIVERSITY OF MARYLAND   (United States)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

^c Stanford University   (United States)

Author keywords

Bayesian networks; Probabilistic relational models; Relational learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA STRUCTURES; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBABILITY DISTRIBUTIONS; STATISTICAL METHODS;

BAYESIAN NETWORKS; EXISTENCE UNCERTAINTY; PROBABILISTIC RELATIONAL MODELS; REFERENCE UNCERTAINTY;

LEARNING ALGORITHMS;

EID: 0141496151     PISSN: 15324435     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (24)

1. S. Chakrabarti, B. Dom, and P. Indyk. Enhanced hypertext categorization using hyperlinks. In Proceedings of ACM SIGMOD International Conference on Management of Data, pages 307-318, Seattle, Washington, 1998.
2. D. Cohn and T. Hofmann. The missing link - a probabilistic model of document content and hypertext connectivity. In Proceedings of Neural Information Processing Systems 13, pages 430-436, Vancouver, British Columbia, 2001.
3. M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam, and S. Slattery. Learning to extract symbolic knowledge from the world wide web. In Proceedings of the Fifteenth Conference of the American Association for Artificial Intelligence, pages 509-516, Madison, Wisconsin, 1998.
4. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 1300-1309, Stockholm, Sweden, 1999.
5. L. Getoor. Learning Statistical Models from Relational Data. PhD thesis, Stanford University, 2001.
6. L. Getoor, N. Friedman, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In S. Dzeroski and N. Lavrac, editors, Relational Data Mining, pages 307-335. Kluwer, 2001.
7. R. Ghani, S. Slattery, and Y. Yang. Hypertext categorization using hyperlink patterns and meta data. In Proceedings of the Eighteenth International Conference on Machine Learning, Williamstown, Massachusetts, 2001.
8. D. Heckerman. A tutorial on learning with Bayesian networks. In. M. I. Jordan, editor, Learning in Graphical Models. MIT Press, Cambridge, Massachusetts, 1998.
9. J. Kleinberg. Authoritative sources in a hyperlinked environment. In Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 668-677, San Francisco, California, 1998.
10. D. Koller and A. Pfeffer. Probabilistic frame-based systems. In Proceedings of the Fifteenth Conference of the American Association for Artificial Intelligence, pages 580-587, Madison, Wisconsin, 1998.
11. N. Lavrač and S. Džeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York, New York, 1994.
12. A. McCallum and K. Nigam. A comparison of event models for naive bayes text classification. In Workshop on Learning for Text Categorization at the Fifteenth Conference of the American Association for Artificial Intelligence, Madison, Wisconsin, 1998.
13. A. McCallum, K. Nigam, J. Rennie, and K. Seymore. Automating the construction of internet portals with machine learning. Information Retrieval, 3(2):127-163, 2000.
14. Muggleton, editor. Inductive Logic Programming. Academic Press, 1992.
15. K. Murphy, Y. Weiss, and M. Jordan. Loopy belief propagation for approximate inference: an empirical study. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 467-475, Stockholm, Sweden, 1999.
16. L. Ngo and P. Haddawy. Probabilistic logic programming and bayesian networks. In Algorithms, Concurrency and Knowledge (Proceedings ACSC95), volume 1023 of Lecture Notes in Computer Science, pages 286-300. Springer-Verlag, 1995.
17. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco, 1988.
18. D. Poole. Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence, 64:81-129, 1993.
19. A. Popescul, L. Ungar, S. Lawrence, and D. Pennock. Towards structural logistic regression: Combining relational and statistical learning. In Proc. KDD-2002 Workshop on Multi-Relational Data Mining. ACM, 2002.
20. S. Slattery and M. Craven. Combining statistical and relational methods for learning in hypertext domains. In David Page, editor, Proceedings of ILP-98, 8th International Conference on Inductive Logic Programming, pages 38-52, Madison, US, 1998. Springer Verlag, Heidelberg, DE.
21. S. Slattery and T. Mitchell. Discovering test set regularities in relational domains. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 895-902, Stanford, California, 2000.
22. B. Taskar, P. Abbeel, and D. Koller. Discriminative probabilistic models for relational data. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, Edmonton, Canada, 2002.
23. B. Taskar, E. Segal, and D. Koller. Probabilistic classification and clustering in relational data. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pages 870-876, Seattle, Washington, 2001.
24. Y. Yang, S. Slattery, and R. Ghani, A study of approaches to hypertext categorization. Journal of Intelligent Information Systems, 18(2-3):219-241, 2002.