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Volumn 42, Issue 1-2, 2006, Pages 84-100

Learning probabilistic decision graphs

Author keywords

Learning; Probabilistic models

Indexed keywords

ALGORITHMS; DATA REDUCTION; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBABILITY;

EID: 33645969314     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2005.10.006     Document Type: Article
Times cited : (31)

References (19)
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    • M. Jaeger, Probabilistic decision graphs: Combining verification and AI techniques for probabilistic inference, in: Proceedings of the first European Workshop on Probabilistic Graphical Models (PGM), 2002, pp. 81-88.
  • 3
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    • Jordan M.I. (Ed), MIT Press
    • In: Jordan M.I. (Ed). Learning in Graphical Models (1999), MIT Press
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    • 33645990866 scopus 로고    scopus 로고
    • C. Boutilier, N. Friedman, M. Goldszmidt, D. Koller, Context-specific independence in Bayesian networks, in: Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence (UAI-96), 1996, pp. 115-123.
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    • F. Provost, T. Fawcett, Analysis and visualization of classifier performance: Comparison under imprecise class and cost distribution, in: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), 1997, pp. 43-48.
  • 12
    • 33645990573 scopus 로고    scopus 로고
    • A. Darwiche, A differential approach to inference in Bayesian networks, in: Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2000), 2000, pp. 123-132.
  • 13
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    • A. Darwiche, A logical approach to factoring belief networks, in: Proceedings of the Eighth International Conference on Principles and Knowledge Representation and Reasoning (KR-2002), 2002, pp. 409-420.
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    • M. Collins, D. McAllester, F. Pereira, Case-factor diagrams for structured probabilistic modeling, in: Proceedings of the Twentieth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2004), 2004, pp. 382-391.
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    • Tree induction for probability-based ranking
    • Provost F., and Domingos P. Tree induction for probability-based ranking. Machine Learning 52 (2003) 199-215
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    • Provost, F.1    Domingos, P.2
  • 17
    • 0000220520 scopus 로고    scopus 로고
    • Learning bayesian networks with local structure
    • Jordan M.I. (Ed), MIT Press
    • Friedman N., and Goldszmidt M. Learning bayesian networks with local structure. In: Jordan M.I. (Ed). Learning in Graphical Models (1999), MIT Press
    • (1999) Learning in Graphical Models
    • Friedman, N.1    Goldszmidt, M.2
  • 18
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    • Multi-terminal binary decision diagrams: an efficient data structure for matrix representation
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  • 19
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    • L.M. de Campos, J.F. Huete, Algorithms for learning decomposable models and chordal graphs, in: Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), 1997, pp. 46-53.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.