Mind change optimal learning of Bayes net structure from dependency and independency data

Information and Computation

Volumn 208, Issue 1, 2010, Pages 63-82

  Schulte, Oliver ^a   Luo, Wei ^b   Greiner, Russell ^c  

^a SIMON FRASER UNIVERSITY   (Canada)

^b UNIVERSITY OF QUEENSLAND   (Australia)

^c UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

APPROACHES TO LEARNING; BAYES NET; CAUSAL REASONING; CONSTRAINT-BASED; CONVERGENCE SPEED; DATA TYPE; FASTER CONVERGENCE; LEARNING PARADIGMS; MIND CHANGES; NP-HARD; NP-HARDNESS; THEORETICAL FRAMEWORK;

COMPUTATIONAL COMPLEXITY; EDUCATION; KNOWLEDGE REPRESENTATION; OPTIMIZATION;

GRAPH THEORY;

EID: 70350716260     PISSN: 08905401     EISSN: 10902651     Source Type: Journal    
DOI: 10.1016/j.ic.2009.03.009     Document Type: Article

References (36)

1. Angluin D. Inductive inference of formal languages from positive data. Information and Control 45 2 (1980) 117-135
2. R.R. Bouckaert, Bayesian belief networks: from construction to inference. Ph.D. Thesis, Universiteit Utrecht, 1995.
3. Cheng J., Greiner R., Kelly J., Bell D., and Liu W. Learning Bayesian networks from data: an information-theory based approach. Artificial Intelligence 137 (2002) 43-90
4. Chickering D. Optimal structure identification with greedy search. Journal of Machine Learning Research 3 (2003) 507-554
5. Chickering D.M., Heckerman D., and Meek C. Large-sample learning of Bayesian networks is NP-hard. Journal of Machine Learning Research 5 (2004) 1287-1330
6. Cooper G. An overview of the representation and discovery of causal relationships using Bayesian networks. In: Glymour C., and Cooper G. (Eds). Computation, Causation, and Discovery (1999), AAAI Press/MIT Press, Cambridge, MA 4-62
7. Cowell R.G., Lauritzen S.L., and Spiegelhater D.J. Probabilistic Networks and Expert Systems (2005), Springer, Berlin
8. de Campos L.M. A scoring function for learning Bayesian networks based on mutual information and conditional independence tests. Journal of Machine Learning Research 7 (2006) 2149-2187
9. Giere R.N. The significance test controversy. The British Journal for the Philosophy of Science 23 2 (1972) 170-181
10. Gold E.M. Language identification in the limit. Information and Control 10 5 (1967) 447-474
11. Jain S., Osherson D., Royer J.S., and Sharma A. Systems that Learn. second ed. (1999), MIT Press, Cambridge, MA
12. Jain S., and Sharma A. Mind change complexity of learning logic programs. TCS 284 1 (2002) 143-160
13. Jensen F.V. Bayesian Networks and Decision Graphs (2002), Springer, Berlin
14. Kelly K. Justification as truth-finding efficiency: how Ockham's razor works?. Minds and Machines 14 4 (2004) 485-505
15. Kelly K. Why probability does not capture the logic of scientific justification. In: Hitchcock C. (Ed). Contemporary Debates in the Philosophy of Science (2004), Wiley-Blackwell, London 94-114
16. W. Luo, O. Schulte, Mind change efficient learning, in: Learning Theory: 18th Annual Conference on Learning Theory, COLT 2005, 2005, pp. 398-412.
17. Luo W., and Schulte O. Mind change efficient learning. Information and Computation 204 (2006) 989-1011
18. Wei Luo, Mind change optimal learning: theory and applications, Ph.D. Thesis, Simon Fraser University, 2007.
19. Martin E., and Osherson D.N. Elements of Scientific Inquiry (1998), MIT Press, Cambridge, MA
20. C. Meek, Graphical models: selecting causal and statistical models, Ph.D. Thesis, Carnegie Mellon University, 1997.
21. R.E. Neapolitan, Learning Bayesian Networks, Pearson Education, 2004.
22. Osherson D.N., Stob M., and Weinstein S. Systems that Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists (1986), MIT Press, Cambridge, MA
23. Papadimitriou C.H. Computational Complexity (1994), Addison-Wesley, Reading, MA
24. Pearl J. Probabilistic Reasoning in Intelligent Systems (1988), Morgan Kauffmann, San Mateo, CA
25. Pearl J. Causality: Models, Reasoning, and Inference (2000), Cambridge University Press, Cambridge, MA
26. Putnam H. Trial and error predicates and the solution to a problem of Mostowski. The Journal of Symbolic Logic 30 1 (1965) 49-57
27. Ramsey J., Zhang J., and Spirtes P. Adjacency-faithfulness and conservative causal inference. Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI 2006) (2006), AUAI Press 401-408
28. Rothe J. Complexity Theory and Cryptology (2005), Springer, Berlin
29. R. Scheines, P. Spirtes, C. Glymour, C. Meek, T. Richardson, TETRAD 3: Tools for Causal Modeling - User's Manual, CMU Philosophy, 1996.
30. Shinohara T. Inductive inference of monotonic formal systems from positive data. New Generation Computing 8 4 (1991) 371-384
31. Spirtes P., Glymour C., and Scheines R. Causation, Prediction, and Search (2000), MIT Press, Cambridge, MA
32. Studeny M. Probabilistic Conditional Independence Structures (2005), Springer, Berlin
33. Tsamardinos I., Brown L.E., and Aliferis C. The max-min hill-climbing Bayesian network structure learning algorithm. Machine Learning 65 1 (2006) 31-78
34. Valiant L., and Vazirani V. NP is as easy as detecting unique solutions. Theoretical Computer Science 47 1 (1986) 85-93
35. T.S. Verma, J. Pearl, Equivalence and synthesis of causal models, in: Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI 1990), 1990, pp. 220-227.
36. Y. Xiang, S.K. Wong, N. Cercone, Critical remarks on single link search in learning belief networks, in: Proceedings of the 12th Annual Conference on Uncertainty in Artificial Intelligence (UAI 1996), 1996, pp. 564-57.