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Volumn , Issue , 2006, Pages 123-130

Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL DISTRIBUTION; DATA AUGMENTATION ALGORITHMS; DATA CLUSTERING; FEATURE VARIABLE; GAUSSIAN MODEL; MIXTURES OF TRUNCATED EXPONENTIALS; NAIVE BAYES; NAIVE BAYES MODELS; NUMBER OF CLASS; SYNTHETIC DATABASE; UNSUPERVISED DATA;

EID: 47649103246     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (8)

References (17)
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    • 0002607026 scopus 로고    scopus 로고
    • Bayesian classification (AUTOCLASS): Theory and results
    • U. M. Fayyad, G. Piatetsky-Shapiro, P Smyth, and R. Uthurusamy, editors AAAI Press/MIT Press
    • P. Cheeseman and J. Stutz. 1996. Bayesian classification (AUTOCLASS): Theory and results. In U. M. Fayyad, G. Piatetsky-Shapiro, P Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 153-180. AAAI Press/MIT Press.
    • (1996) Advances in Knowledge Discovery and Data Mining , pp. 153-180
    • Cheeseman, P.1    Stutz, J.2
  • 3
    • 33745634852 scopus 로고    scopus 로고
    • Approximating probability density functions with mixtures of truncated exponentials
    • In press
    • B.R. Cobb, P.P. Shenoy, and R. Rumí. 2006. Approximating probability density functions with mixtures of truncated exponentials. Statistics and Computing, In press.
    • (2006) Statistics and Computing
    • Cobb, B.R.1    Shenoy, P.P.2    Rumí, R.3
  • 11
    • 84937428461 scopus 로고    scopus 로고
    • Mixtures of truncated exponentials in hybrid Bayesian networks
    • S. Moral, R. Rumí, and A. Salmeŕon. 2001. Mixtures of truncated exponentials in hybrid Bayesian networks. In Lecture Notes in Artificial Intelligence, volume 2143, pages 135-143.
    • (2001) Lecture Notes in Artificial Intelligence , vol.2143 , pp. 135-143
    • Moral, S.1    Rumí, R.2    Salmeŕon, A.3
  • 13
    • 0033685826 scopus 로고    scopus 로고
    • An improved bayesian structural em algorithm for learning bayesian networks for clustering
    • J.M. Peña, J.A. Lozano, and P. Larrañaga. 2000. An improved bayesian structural EM algorithm for learning bayesian networks for clustering. Pattern Recognition Letters, 21:779-786.
    • (2000) Pattern Recognition Letters , vol.21 , pp. 779-786
    • Peña, J.M.1    Lozano, J.A.2    Larrañaga, P.3
  • 15
    • 33947118402 scopus 로고    scopus 로고
    • Bayesian network models of portfolio risk and return
    • W. Lo Y.S. Abu-Mostafa, B. LeBaron and A.S. Weigend, editors MIT Press, Cambridge, MA
    • C. Shenoy and P.P. Shenoy. 1999. Bayesian network models of portfolio risk and return. In W. Lo Y.S. Abu-Mostafa, B. LeBaron and A.S. Weigend, editors, Computational Finance, pages 85-104. MIT Press, Cambridge, MA.
    • (1999) Computational Finance , pp. 85-104
    • Shenoy, C.1    Shenoy, P.P.2
  • 16
    • 84950758368 scopus 로고
    • The calculation of posterior distributions by data augmentation (with discussion)
    • M.A. Tanner and W.H Wong. 1987. The calculation of posterior distributions by data augmentation (with discussion). Journal of the American Statistical Association, 82:528-550.
    • (1987) Journal of the American Statistical Association , vol.82 , pp. 528-550
    • Tanner, M.A.1    Wong, W.H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.