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

Volumn 3060, Issue , 2014, Pages 485-492

Exploring case-based Bayesian networks and Bayesian multi-nets for classification

(2) Hussein, Ahmed a Santos, Eugene a

a University of Connecticut (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); DATA HANDLING; SEMANTICS;

BAYESIAN; BAYESIAN CLASSIFIER; BAYESIAN NETWORKS (BNS); CASE-BASED BAYESIAN NETWORKS; CLASSIFICATION ACCURACY; DOMAIN KNOWLEDGE; EMPIRICAL - COMPARISONS; TRAINING DATA;

BAYESIAN NETWORKS;

EID: 7444238336 PISSN: 03029743 EISSN: 16113349 Source Type: Book Series
DOI: 10.1007/978-3-540-24840-8_42 Document Type: Conference Paper

Times cited : (5)

References (8)

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- Santos, E.¹ Hussein, A.²

2
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- Comparing Bayesian network classifiers
- Cheng, J., Greiner, R.: Comparing bayesian network classifiers. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence. (1999)
- (1999) Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence
- Cheng, J.¹ Greiner, R.²

3
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- Bayesian network classifiers
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- Friedman, N.¹ Geiger, D.² Goldszmidt, M.³

4
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- Learning Bayesian belief network classifiers: Algorithms and system
- Cheng, J., Greiner, R.: Learning bayesian belief network classifiers: Algorithms and system. In: Proceedings of the Fourteenth Canadian Conference on Artificial Intelligence. (2001)
- (2001) Proceedings of the Fourteenth Canadian Conference on Artificial Intelligence
- Cheng, J.¹ Greiner, R.²

5
- 84933530882
- Approximating discrete probability distributions with dependence trees
- Chow, C.K., Liu, C.: Approximating discrete probability distributions with dependence trees. IEEE Transaction on Information Theory 14 (1968) 462-467
- (1968) IEEE Transaction on Information Theory , vol.14 , pp. 462-467
- Chow, C.K.¹ Liu, C.²

6
- 0028482006
- Learning Bayesian belief networks: An approach based on the mdl principle
- Lam, W., Bacchus, F.: Learning bayesian belief networks: An approach based on the mdl principle. Computational Intelligence 10 (1994)
- (1994) Computational Intelligence , vol.10
- Lam, W.¹ Bacchus, F.²

7
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- Learning Bayesian networks from data: An efficient approach based on information theory
- Cheng, J., Bell, D., Liu, W.: Learning bayesian networks from data: An efficient approach based on information theory. In: Proceedings of the Sixth ACM International Conference on Information and Knowledge Managment. (1997)
- (1997) Proceedings of the Sixth ACM International Conference on Information and Knowledge Managment
- Cheng, J.¹ Bell, D.² Liu, W.³

8
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- Extensions to the k-means algorithm for clustering large data sets
- Huang, Z.: Extensions to the k-means algorithm for clustering large data sets. Data Mining and Knowledge Discovery 2 (1998) 283-304
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- Huang, Z.¹

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