Learning from multiple Bayesian networks for the revision and refinement of expert systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2479, Issue , 2002, Pages 82-96

  Borth, Michael ^a  

^a DAIMLER AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EXPERT SYSTEMS; MACHINE LEARNING; UNCERTAINTY ANALYSIS;

COMPLEX DYNAMICS; CONCEPT DISCOVERIES; CONTEXT-SENSITIVE; MODES OF OPERATION; NETWORK FRAGMENTS; NETWORK GENERATION; OPERATIONAL DATA; REASONING UNDER UNCERTAINTY;

BAYESIAN NETWORKS;

EID: 84930181890     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45751-8_6     Document Type: Conference Paper

References (19)

1. F. Jensen, Bayesian Networks and Decision Graphs, Springer, 2001.
2. D. Heckerman, M. Wellman: Real World Applications of Bayesian Networks, in Communications of the ACM, 1995.
3. D. Heckerman, A tutorial on learning with Bayesian Networks, Microsoft Research Report No MSR-TR-95-06, 1995.
4. M. Borth: Die Erstellung von Bayes-Netzen zur Diagnose Technischer Systeme, Thesis, University of Ulm, Germany, 1999 (in German)
5. D. Koller, A. Pfeffer: Object-oriented Bayesian Networks, In Proc. of the 13th Conf. on Uncertainty in Artificial Intelligence, 1997.
6. H. Kargupta, P. Chan (eds.): Advances in Distributed and Parallel Knowledge Discovery, AAAI Press, 2000.
7. F. Provost: Distributed Data Mining: Scaling up and Beyond. In H. Kargupta, P. Chan (eds.): Advances in Distributed and Parallel Knowledge Discovery, AAAI Press, 2000.
8. R. Wirth, M. Borth, J. Hipp: When Distribution is Part of the Semantics. In Proc. of the PKDD 2001 Workshop on Ubiquitous Data Mining for Mobile and Distributed Environments, 2001.
9. J. Pearl: Causality, Cambridge University Press, 2000.
10. K. Thompson, P. Langley: Concept Formation in Structured Domains, in Concept Formation, Morgan Kaufmann, 1991.
11. J. Quinlan: Learning logical definitions from relations. Machine Learning (5) p. 239-266, 1990.
12. K. Laskey, S. Mahoney: Network Fragments: Representing Knowledge for Construction Probabilistic Models Networks, in Proc. of the 13th Conf. on Uncertainty in Artificial Intelligence, 1997.
13. J. Gennari, P. Langley, D. Fisher: Models of incremental concept formation. Artificial Intelligence (40) p. 11-61, 1989.
14. R. Agrawal, T. Imielinski, A. Swami: Mining association rules between sets of items in large databases. In Proc. of the ACM SIGMOD '93, 1993.
15. J. Hipp, U. Güntzer, G. Nakhaeizadeh: Algorithms for Association Rule Mining – A General Survey and Comparison. SIGKDD Explorations 2 (1), 2000.
16. M. Gluck, J. Corter: Information, uncertainty and the utility of categories. In Proc. of the 7th Annual Conf. of the Cognitive Science Society, 1985.
17. D. Fisher: Iterative Optimization and Simplification of Hierarchical Clusterings, JAIR, vol 4 p. 147-179, 1996.
18. H. Ellis: Human Learning and Cognition, 1972
19. I. Beinlich, H. J. Suermondt, R. M. Chavez, G. F. Cooper: The ALARM monitoring system. In Proc. of the 2nd European Conf. on Artificial Intelligence in Medicine, 1989