Incremental augmented naive bayes classifiers

Frontiers in Artificial Intelligence and Applications

Volumn 110, Issue , 2004, Pages 539-543

  Alcobe, Josep Roure ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; LEARNING SYSTEMS;

BAYESIAN NETWORK STRUCTURE; COMPUTING TIME; CURRENT STRUCTURE; HILL CLIMBING SEARCH; INCREMENTAL APPROACH; NAIVE BAYES CLASSIFIERS; NETWORK STRUCTURE LEARNING; STRUCTURE LEARNING ALGORITHM;

CLASSIFIERS;

EID: 85017336552     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (14)

1. R.R. Bouckaert, Bayesian belief networks: from inference to construction, Ph.D. dissertation, Utrecht University, 1995.
2. W. Buntine, Theory refinement on Bayesian networks, in Proceedings of the 7th UAI, (1991).
3. Jie Cheng and Russell Greiner, Learning Bayesian belief network classifiers: Algorithms and systems, in Proceedings of the Canadian Conference on AI, pp. 141-151, (2001).
4. C.K. Chow and C.N. Liu, Approximating discrete probability distributions with dependence trees, IEEE Transactions on Information Teory, 14, 462-467, (1968).
5. Domingos and Hulten, A general framework for mining massive data streams, Journal of Computational and Graphical Statistics, 12, (2003).
6. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, John Wiley & Sons, New York, 2nd edn., 2001.
7. N. Friedman and M. Goldszmidt, Sequential update of Bayesian network structure, in Proceedings of the 13th UAI, (1997).
8. Nir Friedman, Dan Geiger, and Moises Goldszmidt, Bayesian network classifiers, Machine Learning, 29(2-3), 131-163, (1997).
9. R. Kohavi, G. John, R. Long, D. Manley, and K. Pfleger, MLC++: A Machine Learning library in c++, in Proceedings of the Sixth International Conference on Tools with Artificial Intelligence, pp. 740-743. IEEE Computer Society Press, (1994).
10. W. Lam and F. Bacchus, Learning Bayesian belief networks. an approach based on the MDL principle, Computational Intelligence, 10(4), 269-293, (1994).
11. Pat Langley and Stephanie Sage, Induction of selective Bayesian classifiers, in Proceedings of the tenth Conference on Uncertainty in Artificial Intelligence (UAI94), eds., R. Ĺopez de Mantaras and D. Poole, pp. 399-406. San Francisco, CA: Morgan Kaufmann, (1994).
12. Andrew W. Moore and Mary S. Lee, Cached sufficient statistics for efficient machine learning with large datasets, Journal of A.I. Research, 8, 67-91, (1998).
13. P.M. Murphy and D.W. Aha. UCI repository of Machine Learning databases. http://www.ics.uci.edu/mlearn/MLRepository.html, 1994.
14. D. J. Spiegelhalter and S. L. Lauritzen, Sequential updating of conditional probabilities on directed graphical structures, Networks, 20, 579-605, (1990).