Symmetric causal independence models for classification

Proceedings of the 3rd European Workshop on Probabilistic Graphical Models, PGM 2006

Volumn , Issue , 2006, Pages 163-170

  Jurgelenaite, Rasa ^a   Heskes, Tom ^a  

^a RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

CAUSAL INDEPENDENCE; CAUSAL INDEPENDENCE MODEL; CLASSIFICATION PERFORMANCE; DATA COLLECTION; EM ALGORITHMS; PROBABILITY TABLES; REUTERS; SYMMETRIC BOOLEAN FUNCTIONS; UNDERLYING MECHANISM;

ALGORITHMS; BAYESIAN NETWORKS;

BOOLEAN FUNCTIONS;

EID: 84874746186     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. S. Boyd and L. Vandenberghe. 2004. Convex Optimization. Cambridge University Press.
2. J. Darroch. 1964. On the distribution of the number of successes in independent trials. The Annals of Mathematical Statistics, 35:1317-1321.
3. A.P. Dempster, N.M. Laird and D.B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39:1-38.
4. F.J. Díez. 1993. Parameter adjustment in Bayes networks. The generalized noisy OR-gate. In Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, pages 99-105.
5. H.B. Enderton. 1972. A Mathematical Introduction to Logic. Academic Press, San Diego.
6. D. Heckerman and J.S. Breese. 1994. A new look at causal independence. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pages 286-292.
7. M. Henrion. 1989. Some practical issues in constructing belief networks. Uncertainty in Artificial Intelligence, 3:161-173.
8. R. Jurgelenaite, T. Heskes and P.J.F. Lucas. 2006. Noisy threshold functions for modelling causal independence in Bayesian networks. Technical report ICIS-R06014, Radboud University Nijmegen.
9. H.J. Kappen and J.P. Neijt. 2002. Promedas, a probabilistic decision support system for medical diagnosis. Technical report, SNN-UMCU.
10. G. Kařciauskas. 2002. Text Categorization using Hierarchical Bayesian Network Classifiers. Master thesis, Aalborg University.
11. L. Le Cam. 1960. An approximation theorem for the Poisson binomial distribution. Pacific Journal of Mathematics, 10:1181-1197.
12. P.J.F. Lucas. 2005. Bayesian network modelling through qualitative patterns. Artificial Intelligence, 163:233-263.
13. C. Meek and D. Heckerman. 1997. Structure and parameter learning for causal independence and causal interaction models. In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, pages 366-375.
14. J. Pearl. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kauffman Publishers.
15. M.A. Shwe, B. Middleton, D.E. Heckerman, M. Henrion, E.J. Horvitz, H.P. Lehmann and G.F. Cooper. 1991. Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base, I-the probabilistic model and inference algorithms. Methods of Information in Medicine, 30:241-255.
16. S. Srinivas. 1993. A generalization of the noisy-or model. In Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, pages 208-215.
17. S. Visscher, P.J.F. Lucas, M. Bonten and K. Schurink. 2005. Improving the therapeutic performance of a medical Bayesian network using noisy threshold models. In Proceedings of the Sixth International Symposium on Biological and Medical Data Analysis, pages 161-172.
18. J. Vomlel. 2006. Noisy-or classifier. International Journal of Intelligent Systems, 21:381-398.
19. I. Wegener. 1987. The Complexity of Boolean Functions. John Wiley & Sons, New York.
20. N.L. Zhang and D. Poole. 1996. Exploiting causal independence in Bayesian networks inference. Journal of Artificial Intelligence Research, 5:301-328.