Learning discrete Bayesian network parameters from continuous data streams: What is the best strategy?

Journal of Applied Logic

Volumn 13, Issue 4, 2015, Pages 628-642

  Ratnapinda, Parot ^a,b   Druzdzel, Marek J ^a,c  

^a UNIVERSITY OF PITTSBURGH   (United States)

^b MAEJO UNIVERSITY   (Thailand)

^c BIALYSTOK UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Bayesian networks; EM algorithm; Parameter learning

Indexed keywords

DATA STREAMS; LEARNING ALGORITHMS; PARAMETER ESTIMATION;

APPROACHES TO LEARNING; COMPUTATIONAL LIMITATIONS; EM ALGORITHMS; INCREMENTAL ALGORITHM; INCREMENTAL LEARNING; MODEL CLASSIFICATION; NUMERICAL PARAMETERS; PARAMETER LEARNING;

BAYESIAN NETWORKS;

EID: 84947489474     PISSN: 15708683     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jal.2015.03.007     Document Type: Conference Paper

References (20)

1. J.M. Agosta, T.R. Gardos, and M.J. Druzdzel Query-based diagnostics M. Jaeger, T.D. Nielsen, Proceedings of the Fourth European Workshop on Probabilistic Graphical Models PGM-08, Aalborg, Denmark 2008 1 8
2. O. Cappe Online expectation maximisation K.L. Mengersen, C.P. Robert, D.M. Titterington, Mixtures: Estimation and Applications 2011 John Wiley & Sons, Ltd. 31 53
3. L. Carbonara, and A. Borrowman A comparison of batch and incremental supervised learning algorithms Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery PKDD'98 1998 Springer-Verlag London, UK 264 272
4. G.F. Cooper The computational complexity of probabilistic inference using Bayesian belief networks Artif. Intell. 42 1990 393 405
5. G.F. Cooper, and E. Herskovits A Bayesian method for the induction of probabilistic networks from data Mach. Learn. 9 1992 309 347
6. A. Dempster, N. Laird, and D. Rubin Maximum likelihood from incomplete data via the EM algorithm J. R. Stat. Soc., Ser. B 39 1977 1 38
7. A. Frank, and A. Asuncion UCI Machine Learning Repository http://archive.ics.uci.edu/ml 2010
8. J. Gama Knowledge Discovery from Data Streams 2010 Chapman & Hall/CRC
9. H. He, S. Chen, K. Li, and X. Xu Incremental learning from stream data IEEE Trans. Neural Netw. 22 2011 1901 1914
10. G. Holmes, R. Kirkby, and D. Bainbridge Batch incremental learning for mining data streams Working Paper 2004 Department of Computer Science, University of Waikato http://researchcommons.waikato.ac.nz/handle/10289/1749
11. G. Kokolakis, and P. Nanopoulos Bayesian multivariate micro-aggregation under the Hellinger's distance criterion Res. Off. Stat. 4 2001 117 126
12. S. Kullback, and R.A. Leibler On information and sufficiency Ann. Math. Stat. 22 1951 79 86
13. S.L. Lauritzen The EM algorithm for graphical association models with missing data Comput. Stat. Data Anal. 19 1995 191 201
14. P. Liang, and D. Klein Online EM for unsupervised models Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics NAACL'09 2009 Association for Computational Linguistics Stroudsburg, PA, USA 611 619
15. A. Łupińska-Dubicka, and M.J. Druzdzel A comparison of popular fertility awareness methods to a DBN model of the woman's monthly cycle The Sixth European Workshop on Probabilistic Graphical Models PGM 2012 2012 219 226
16. K. Olesen, S. Lauritzen, and F. Jensen aHUGIN: a system creating adaptive causal probabilistic networks Proceedings of the Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence UAI-92 1992 Morgan Kaufmann San Mateo, CA 223 229
17. A. Onis̈ko, and M.J. Druzdzel Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems Artif. Intell. Med. 57 2013 197 206
18. J. Pearl Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference 1988 Morgan Kaufmann Publishers, Inc. San Mateo, CA
19. M.-A. Sato, and S. Ishii On-line EM algorithm for the normalized Gaussian network Neural Comput. 12 2000 407 432
20. D.R. Wilson, and T.R. Martinez The general inefficiency of batch training for gradient descent learning Neural Netw. 16 2003 1429 1451