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Volumn 59, Issue 1, 2007, Pages 3-25

A modified em algorithm for mixture models based on Bregman divergence

Author keywords

Bregman divergence; EM algorithm; Finite mixture models

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; MATHEMATICAL MODELS; NONLINEAR SYSTEMS; OPTIMIZATION; PROBLEM SOLVING; STATISTICAL METHODS;

EID: 33847231874     PISSN: 00203157     EISSN: 15729052     Source Type: Journal    
DOI: 10.1007/s10463-006-0097-x     Document Type: Article
Times cited : (19)

References (15)
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  • 2
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  • 9
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    • Meng, X.L.1    Rubin, D.B.2
  • 10
    • 0040673441 scopus 로고    scopus 로고
    • Robust blind source separation by beta divergence
    • Minami, M., Eguchi, S. (2002). Robust blind source separation by beta divergence. Neural Computation, 14, 1859-1886.
    • (2002) Neural Computation , vol.14 , pp. 1859-1886
    • Minami, M.1    Eguchi, S.2
  • 11
    • 2942627097 scopus 로고    scopus 로고
    • Information geometry of U-Boost and Bregman divergence
    • Murata, N., Takenouchi, T., Kanamori, T., Eguchi, S. (2004). Information geometry of U-Boost and Bregman divergence. Neural Computation, 16(7), 1437-1481.
    • (2004) Neural Computation , vol.16 , Issue.7 , pp. 1437-1481
    • Murata, N.1    Takenouchi, T.2    Kanamori, T.3    Eguchi, S.4
  • 12
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    • Robustifying AdaBoost by adding the naive error rate
    • Takenouchi, T., Eguchi, S. (2004). Robustifying AdaBoost by adding the naive error rate. Neural Computation, 16(4), 767-787.
    • (2004) Neural Computation , vol.16 , Issue.4 , pp. 767-787
    • Takenouchi, T.1    Eguchi, S.2
  • 15
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    • EM algorithm in neural network learning
    • N. Murata, S. Ikeda Eds, New York: Marcel Dekker
    • Watanabe, M., Yamaguchi, K. (2004). EM algorithm in neural network learning. In N. Murata, S. Ikeda (Eds.) The EM algorithm and related statistical models (pp. 95-125). New York: Marcel Dekker.
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    • Watanabe, M.1    Yamaguchi, K.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.