Efficient discriminative learning of Bayesian network classifier via Boosted Augmented Naive Bayes

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

Volumn , Issue , 2005, Pages 369-376

  Jing, Yushi ^a   Pavlović, Vladimir ^b   Rehg, James M ^a  

^a Georgia Institute of Technology   (United States)

^b RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; DATA REDUCTION; LEARNING SYSTEMS; OPTIMIZATION; PARAMETER ESTIMATION;

BAYESIAN NETWORK CLASSIFIER; BOOSTED AUGMENTED NAIVE BAYES; NAIVE BAYES (BAN) CLASSIFIER; STRUCTURE LEARNING SHOW PROMISE;

COMPUTER NETWORKS;

EID: 31844453166     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102398     Document Type: Conference Paper

References (14)

1. Blake, C., & Merz, C. (1998). UCI repository of machine learning databases.
2. Chow, C. K., & Liu, C. N. (1968). Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, IT-14, 462-467.
3. Cooper, G. F., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347.
4. Elkan, C. (1997). Boosting and naive bayesian learning (Technical Report). Department of Computer Science and Engineering, University of California, San Diego.
5. Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting. The Annals of Statistics, 38, 337-374.
6. Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29, 131-163.
7. Greiner, R., & Zhou, W. (2002). Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers. Proceedings of annual meeting of the American Association for Artificial Intelligence (pp. 167-173).
8. Grossman, D., & Domingos, P. (2004). Learning bayesian network classifiers by maximizing conditional likelihood. Proc. 21st International Conference on Machine Learning (pp. 361-368). Banff, Canada: ACM Press.
9. Heckerman, D. (1999). A tutorial on learning with bayesian networks. 301-354.
10. Ng, A. Y., & Jordan, M. I. (2002). On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Proc. 14th Conference on Advances in Neural Information Processing Systems (pp. 841-848). Cambridge, MA: MIT Press.
11. Ridgeway, G., Madigan, D., Richardson, T., & O'Kane, J. (1998). Interpretable boosted naive bayes classification. Proceedings Fourth International Conference on Knowledge Discovery and Data Mining.
12. Schapire, R. E., & Singer, Y. (1998). Improved boosting algorithms using confidence-rated predictions. Proc. 11th Annual Conference on Computational Learning. Theory.
13. Webb, G., & Zheng, Z. (2004). Multistrategy ensemble learning: Reducing error by combining ensemble learning techniques. IEEE Transactions on Knowledge and Data Engineering, 16, 980-991.
14. Webb, G. I., Boughton, J., & Wang, Z. (2005). Not so naive bayes: Aggregating one-dependence estimators. Machine Learning, 58, 5-24.