A study on reliability in graph discovery

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2006, Pages 775-779

  Dai, Honghua ^a  

^a DEAKIN UNIVERSITY   (Australia)

Author keywords

Bayesian network; Machine learning; Reliability; Reliable discovery; Small samples; Weak links

Indexed keywords

BAYESIAN NETWORKS; COMPLEX NETWORKS; LEARNING SYSTEMS; RELIABILITY; SAMPLING;

CRITICAL QUESTIONS; DATA MINING SYSTEM; GRAPHICAL MODEL; MEASUREMENTS OF; MODEL COMPLEXITY; RELIABLE DISCOVERY; SMALL SAMPLES; WEAK LINKS;

DATA MINING;

EID: 62449208603     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/icdmw.2006.23     Document Type: Conference Paper

References (10)

1. American Educational Research Association, American Psychological Association, and National Council on Measurement in Education. Stardards for educational and psychological testing. American Educational Research Association, iWashington DC:Authors, 1985.
2. Honghua Dai. Learning of forecasting rules from large noisy meteorological data. PhD. Dissertation, Department of Computer Science, RMIT, 1994.
3. David Heckerman. A Bayesian Approach to Learning Causal Networks. Proceedings of 11th Conference on Uncertainty in Artificial Intelligence, pages 285-295, 1995.
4. David Heckerman, Dan Geiger, and David M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3):197-243, 1995.
5. David Madigan, Steen A. Andersson, Michael D. Perlman, and Chris T. Volinsky. Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs. NIPS 95 Workshop on Learning in Bayesian Networks and Other Graphical Models, 1995.
6. Judea Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, California, 1988.
7. Judea Pearl and T. S. Verma. A theory of inferred causation. In Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, pages 441-452, San Mateo, California, April 22-25, 1991. Morgan Kaufmann Publishers.
8. Glen Shafer. Probabilistic Expert Systems. SIAM Press, 1996.
9. Joe Suzuki. Learning bayesian belif networks based on the minimum description length principle: An efficient algorithm using the B & B techniques. In Lorenza Saitta, editor, Proceedings of the 13th International Conference on Machine Learning, pages 462-470, 340 Pine St., 6th Floor, San Francisco, July, 1996. Morgan Kaufmann.
10. Chris Wallace, Kevin Korb, and Honghua Dai. Causal discovery via MML. In Proceedings of the 13th International Conference on Machine Learning (ICML'96), pages 516-524, 1996.