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Volumn , Issue , 2005, Pages 529-536

Naive Bayes models for probability estimation

Author keywords

[No Author keywords available]

Indexed keywords

PARAMETER ESTIMATION; PROBABILITY; REAL TIME SYSTEMS; SAMPLING;

EID: 31844452069     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102418     Document Type: Conference Paper
Times cited : (240)

References (16)
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  • 3
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    • Bayesian classification (AutoClass): Theory and results
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  • 4
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    • (Technical Report MSR-TR-2002-103). Microsoft, Redmond, WA
    • Chickering, D. M. (2002). The WinMine Toolkit (Technical Report MSR-TR-2002-103). Microsoft, Redmond, WA.
    • (2002) The WinMine Toolkit
    • Chickering, D.M.1
  • 6
    • 0031269184 scopus 로고    scopus 로고
    • On the optimality of the simple Bayesian classifier under zero-one loss
    • Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103-130.
    • (1997) Machine Learning , vol.29 , pp. 103-130
    • Domingos, P.1    Pazzani, M.2
  • 7
    • 0000854197 scopus 로고    scopus 로고
    • The Bayesian structural em algorithm
    • Friedman, N. (1998). The Bayesian structural EM algorithm. Proc. UAI-98 (pp. 129-138).
    • (1998) Proc. UAI-98 , pp. 129-138
    • Friedman, N.1
  • 8
    • 0000319411 scopus 로고    scopus 로고
    • Learning Bayesian networks with local structure
    • Friedman, N., & Goldszmidt, M. (1996). Learning Bayesian networks with local structure. Proc. UAI-96 (pp. 252-262).
    • (1996) Proc. UAI-96 , pp. 252-262
    • Friedman, N.1    Goldszmidt, M.2
  • 10
    • 0002549585 scopus 로고    scopus 로고
    • Eigentaste: A constant time collaborative filtering algorithm
    • Goldberg, K., Roeder, T., Gupta, D., & Perkins, C. (2001). Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4(2), 133-151.
    • (2001) Information Retrieval , vol.4 , Issue.2 , pp. 133-151
    • Goldberg, K.1    Roeder, T.2    Gupta, D.3    Perkins, C.4
  • 11
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statist, data
    • Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statist, data. Machine Learning, 20, 197-243.
    • (1995) Machine Learning , vol.20 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.M.3
  • 15
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    • On the hardness of approximate reasoning
    • Roth, D. (1996). On the hardness of approximate reasoning. Artificial Intelligence, 82, 273-302.
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  • 16
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    • Generalized belief propagation
    • Yedidia, J. S., Freeman, W. T., & Weiss, Y. (2001). Generalized belief propagation. In Adv. NIPS 13, 689-695.
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    • Yedidia, J.S.1    Freeman, W.T.2    Weiss, Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.