Practical aspects of efficient forward selection in decomposable graphical models

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn , Issue , 2004, Pages 710-715

  Altmueller, Stephan M ^a   Haralick, Robert M ^a  

^a CITY UNIVERSITY OF NEW YORK   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATASETS; DECOMPOSABLE GRAPHICAL MODELS; FORWARD SELECTION; HANDWRITTEN DIGITS;

ALGORITHMS; BIT ERROR RATE; DATA REDUCTION; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PROBABILITY DISTRIBUTIONS; PROBLEM SOLVING; STATISTICS;

GRAPH THEORY;

EID: 16244387639     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2004.100     Document Type: Conference Paper

References (8)

1. S. Altmueller and R. Haralick. Estimating high dimensional probability distributions. In Internationl Conference in Pattern Recognition (ICPR), 2004.
2. S. Altmueller and R. Haralick. Forward selection in decomposable graphical models. Technical report, CUNY Graduate Center, 2004.
3. C. Chow and C. Liu. Approximating discrete probability distributions with dependence trees. IEEE Trans. Information Theory, 14:(11):462-467, November 1968.
4. A. Deshpande, M. Garofalakis, and M. I. Jordan. Efficient stepwise selection in decomposable models. In Uncertainty in Artificial Intelligence, 2001.
5. D. Heckerman, D. Geiger, and D. Chickering. Learning bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197-243, 1995.
6. W. Lam and F. Bacchus. Learning bayesian belief networks: An approach based on the mdl principle. Computational Intelligence, 10:269-293, 1994.
7. S. Lauritzen. Graphical Models. Oxford University Press, 1996.
8. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, Gradientbased learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, November 1998.