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Volumn 3025, Issue , 2004, Pages 210-219

Incremental mixture learning for clustering discrete data

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FUNCTIONS; OPTIMIZATION; PARAMETER ESTIMATION; PROBLEM SOLVING; SET THEORY; ARTIFICIAL INTELLIGENCE; IMAGE SEGMENTATION; MAXIMUM PRINCIPLE; MIXTURES;

EID: 9444252298     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-24674-9_23     Document Type: Conference Paper
Times cited : (5)

References (8)
  • 1
    • 0002607026 scopus 로고
    • Bayesian classification (AutoClass): Theory and resutls
    • U. Fayyad, G. Piatesky-Shapiro, P. Smyth, and R. Uthurusamy, editors. CA: AAAI Press
    • Cheeseman P. and Stutz J. Bayesian classification (AutoClass): Theory and resutls. In U. Fayyad, G. Piatesky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 153-180. CA: AAAI Press, 1995.
    • (1995) Advances in Knowledge Discovery and Data Mining , pp. 153-180
    • Cheeseman, P.1    Stutz, J.2
  • 2
    • 0142223338 scopus 로고    scopus 로고
    • Modeling high-dimensional discrete data with multi-layer neural networks
    • S.A. Solla, T.K. Leen, and K.-R. Móller, editors. MIT Press
    • Bengio Y. and Bengio S. Modeling high-dimensional discrete data with multi-layer neural networks. In S.A. Solla, T.K. Leen, and K.-R. Móller, editors, Advances in Neural Processing Systems 12, pages 400-406. MIT Press, 2000.
    • (2000) Advances in Neural Processing Systems 12 , pp. 400-406
    • Bengio, Y.1    Bengio, S.2
  • 3
    • 0034826101 scopus 로고    scopus 로고
    • An experimental comparison of model-based clustering methods
    • Meilǎ M. and Hecherman D. An experimental comparison of model-based clustering methods. Machine Learning, 42:9-29, 2001.
    • (2001) Machine Learning , vol.42 , pp. 9-29
    • Meilǎ, M.1    Hecherman, D.2
  • 4
    • 0037461026 scopus 로고    scopus 로고
    • Greedy mixture learning for multiple motif discovering in biological sequences
    • Blekas K., Fotiadis D.I., and Likas A. Greedy mixture learning for multiple motif discovering in biological sequences. Bioinformatics, 19(5):607-617, 2003.
    • (2003) Bioinformatics , vol.19 , Issue.5 , pp. 607-617
    • Blekas, K.1    Fotiadis, D.I.2    Likas, A.3
  • 5
    • 0031272327 scopus 로고    scopus 로고
    • Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables
    • Chickering D. and Heckerman D. Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Machine Learning, 29:181-212, 1997.
    • (1997) Machine Learning , vol.29 , pp. 181-212
    • Chickering, D.1    Heckerman, D.2
  • 6
    • 0021404166 scopus 로고
    • Mixture densities, maximum likelihood and the EM algorithm
    • Render R.A. and Walker H.F. Mixture densities, maximum likelihood and the EM algorithm. SIAM Review, 26(2): 195-239, 1984.
    • (1984) SIAM Review , vol.26 , Issue.2 , pp. 195-239
    • Render, R.A.1    Walker, H.F.2
  • 7
    • 0036469527 scopus 로고    scopus 로고
    • A greedy EM algorithm for Gaussian mixture learning
    • Vlassis N. and Likas A. A greedy EM algorithm for Gaussian mixture learning. Neural Processing Letters, 15:77-87, 2002.
    • (2002) Neural Processing Letters , vol.15 , pp. 77-87
    • Vlassis, N.1    Likas, A.2
  • 8
    • 0016557674 scopus 로고
    • Multidimensional binary search trees used for associative searching
    • Bentley J.L. Multidimensional binary search trees used for associative searching. Commun. ACM, 18(9):509-517, 1975.
    • (1975) Commun. ACM , vol.18 , Issue.9 , pp. 509-517
    • Bentley, J.L.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.