Learning probabilistic relational models research summary

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 1864, Issue , 2000, Pages 322-323

  Getoor, Lise ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84942908828     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-44914-0_25     Document Type: Conference Paper

References (7)

1. G. F. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309-347, 1992.
2. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In Proc. IJCAI, 1999.
3. L. Getoor, D. Koller, B. Taskar, and N. Friedman. Discovering probablisitic models of relational structure. 2000. unpublished.
4. D. Heckerman. A tutorial on learning with Bayesian networks. In M. I. Jordan, editor, Learning in Graphical Models. MIT Press, Cambridge, MA, 1998.
5. D. Koller and A. Pfeffer. Probabilistic frame-based systems. In Proc. AAAI, 1998.
6. W. Lam and F. Bacchus. Learning Bayesian belief networks: An approach based on the MDL principle. Computational Intelligence, 10:269-293, 1994.
7. A. Pfeffer. Probabilistic Reasoning for Complex Systems. PhD thesis, Stanford University, 2000.