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Volumn 4013 LNAI, Issue , 2006, Pages 503-514

Learning naive Bayes for probability estimation by feature selection

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; LEARNING ALGORITHMS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; PROBABILITY;

EID: 33746098151     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11766247_43     Document Type: Conference Paper
Times cited : (13)

References (20)
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    • Assessing the calibration of Naive Bayes' posterior estimates
    • Bennett, P. N.: Assessing the calibration of Naive Bayes' posterior estimates. Technical Report No. CMU-CS00-155 (2000)
    • (2000) Technical Report No. CMU-CS00-155 , vol.CMU-CS00-155
    • Bennett, P.N.1
  • 2
    • 0001019707 scopus 로고    scopus 로고
    • Learning Bayesian networks is NP-complete
    • Fisher, D. and Lenz, H., editors. Springer-Verlag
    • Chickering, D. M. (1996). Learning Bayesian networks is NP-Complete. In Fisher, D. and Lenz, H., editors, Learning from Data: Artificial Intelligence and Statistics V, pages 121-130. Springer-Verlag.
    • (1996) Learning from Data: Artificial Intelligence and Statistics V , pp. 121-130
    • Chickering, D.M.1
  • 3
    • 0041919126 scopus 로고    scopus 로고
    • The WinMine toolkit
    • Chickering, D. M.: The WinMine Toolkit. Technical Report MSR-TR-2002-103 (2002)
    • (2002) Technical Report , vol.MSR-TR-2002-103
    • Chickering, D.M.1
  • 4
    • 0031269184 scopus 로고    scopus 로고
    • Beyond independence: Conditions for the optimality of the simple bayesian classifier
    • Domingos, P., Pazzani M.: Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. Machine Learning 20 (1997) 103-130
    • (1997) Machine Learning , vol.20 , pp. 103-130
    • Domingos, P.1    Pazzani, M.2
  • 7
    • 0031276011 scopus 로고    scopus 로고
    • Bayesian network classifiers
    • Priedman, Geiger, and Goldszmidt. "Bayesian Network Classifiers", Machine Learning, Vol. 29, 131-163, 1997.
    • (1997) Machine Learning , vol.29 , pp. 131-163
    • Priedman, G.1    Goldszmidt2
  • 8
    • 0036927090 scopus 로고    scopus 로고
    • Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers
    • AAAI Press
    • R. Greiner, W. Zhou: Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers. Proceedings of the Eighteenth National Conference on Artificial Intelligence (pp. 167-173), 2002. AAAI Press.
    • (2002) Proceedings of the Eighteenth National Conference on Artificial Intelligence , pp. 167-173
    • Greiner, R.1    Zhou, W.2
  • 9
    • 21244444642 scopus 로고    scopus 로고
    • Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers
    • R. Greiner, X. Su, B. Shen, and W. Zhou: Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers. Machine Learning, 59(3), 2005.
    • (2005) Machine Learning , vol.59 , Issue.3
    • Greiner, R.1    Su, X.2    Shen, B.3    Zhou, W.4
  • 18
    • 0042346121 scopus 로고    scopus 로고
    • Tree induction for probability-based ranking
    • Provost, F. J., Domingos, P.: Tree Induction for Probability-Based Ranking. Machine Learning 52(3) (2003) 199-215
    • (2003) Machine Learning , vol.52 , Issue.3 , pp. 199-215
    • Provost, F.J.1    Domingos, P.2
  • 19
    • 84941154912 scopus 로고    scopus 로고
    • http://prdownloads.sourceforge.net/weka/datasets-UCI.jar


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.