Discriminative Parameter Learning of General Bayesian Network Classifiers

Proceedings of the International Conference on Tools with Artificial Intelligence

Volumn , Issue , 2003, Pages 296-305

  Shen, Bin ^a   Su, Xiaoyuan ^a   Greiner, Russell ^a   Musilek, Petr ^a   Cheng, Corrine ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BENCHMARKING; CLASSIFICATION (OF INFORMATION); MAXIMUM LIKELIHOOD ESTIMATION; OPTIMIZATION; STANDARDS;

BAYESIAN BELIEF NETWORK (BN); DISCRIMINATIVE PARAMETER LEARNING; LOGISTIC REGRESSION;

LEARNING SYSTEMS;

EID: 0345097610     PISSN: 10636730     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. R. Greiner and W. Zhou. Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers. AAAI, 2002.
2. J. Cheng and R. Greiner. Learning Bayesian Belief Network Classifiers: Algorithms and System, CSCS101, 2001.
3. R. Duda and P. Hart. Pattern classification and scene analysis. New York, NY: Wiley, 1973.
4. B. Ripley. Pattern Recognition in Intelligence Systems: Networks of Plausible Inference. Morgan Kaufmann, 1998.
5. M. Jordan. Why the logistic function? A tutorial discussion on probabilities and neural networks, 1995.
6. G. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from the data. Machine Learning, 9:309-347, 1992.
7. D. Heckerman. A Tutorial on Learning with Bayesian Networks. Learning in Graphical Models, 1998.
8. J. Binder, D. Koller, S. Russell, and K. Kanazawa. Adaptive probabilistic networks with hidden variables. Machine Learning, 29:213-244, 1997.
9. N. Friedman, D. Geiger and M. Goldszmidt. Bayesian Network Classifiers. Machine Learning 29:131-163, 1997.
10. I. Beinlich, H. Suermondt, R. Chavez, and G. Cooper. The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. AIME-89, pages 247-256. Springer Verlag, Berlin, 1989.
11. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
12. C. Chow and C. Liu. Approximating discrete probability distributions with dependence trees. IEEE Trans. on Information Theory, 14:462-467, 1968.
13. D. Heckerman. A Bayesian Approach to Learning Causal Networks. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence (pp. 285-295), Morgan Kaufmann, August 1995.
14. J. Cheng, R. Greiner and J. Kelly et al. Learning Bayesian network from data: An information-theory based approach. Artificial Intelligence Journal 137:43-90, 2002.
15. R. Greiner, A. Grove and D. Schuurmans. Learning Bayesian Nets that Perform Well. UA197, August 1997.
16. P. McCullagh and J. Nelder. Generalized Linear Models. Chapman and Hall, 1989.
17. T. Minka, Algorithms for maximum-likelihood logistic regression. Statistics Technical Report 758, CMU, 2001.
18. T. Mitchell. Machine Learning. McGraw-Hill, 1997.
19. UCI Repository of Machine Learning Databases and Domain Theories, 1995. http://www.ics.uci.edu/̃mlearn/MLRepository.html
20. R. Kohavi and G. John. Wrappers for feature subset selection. Artificial Intelligence, 97:1-2, 1997.
21. U. Fayyad and K. Irani. Multi-interval discretization of continuous-valued attributes for classification learning. In IJCAI, (pp. 1022-1027), 1993.
22. P. Domingo and M. Pazzani. Beyond independence: conditions for the optimality of the simple Bayesian classifier. Proc.13th International Conference on Machine Learning, 1996.
23. A. Ng and M. Jordan. On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naïve Bayes, NIPS 15, 2003.
24. E. Herskovits and C. Cooper. Algorithms for Bayesian belief-network precomputation. In Methods of Information in Medicine (pp. 362-370), 1991.
25. D. Grossman and P. Domingos. Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood, Department of Computer Science and Engineering, University of Washington, Technical Report, 2003.
26. C. Ling, J. Huang, and H. Zhang. AUC: a Statistically Consistent and more Discriminating Measure than Accuracy. Proceedings of IJCAI 2003.