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Volumn 10, Issue 1, 1997, Pages 99-109

Density estimation through convex combinations of densities: Approximation and estimation bounds

Author keywords

approximation error; convergence rates; density estimation; maximum likelihood; mixture models

Indexed keywords

DENSITY ESTIMATION; MAXIMUM LIKELIHOOD; MIXTURE MODELS;

EID: 0031037819     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(96)00037-8     Document Type: Article
Times cited : (62)

References (17)
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  • 2
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  • 3
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    • (1993) IEEE Transactions on Information Theory , vol.IT-39 , pp. 930-945
    • Barron, A.R.1
  • 4
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    • Approximation and estimation bounds for artificial neural networks
    • Barron, A. R. (1994). Approximation and estimation bounds for artificial neural networks. Machine Learning, 4, 115-133.
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    • Barron, A.R.1
  • 5
    • 0000404653 scopus 로고
    • Approximation of density functions by sequences of exponential families
    • Barron, A. R., & Sheu, C. H. (1991). Approximation of density functions by sequences of exponential families. Annals of Statistics, 1(3), 1347-1369.
    • (1991) Annals of Statistics , vol.1 , Issue.3 , pp. 1347-1369
    • Barron, A.R.1    Sheu, C.H.2
  • 10
    • 0001414599 scopus 로고
    • Nonparametric maximum likelihood estimation by the method of sieves
    • Geman, S., & Gwang, C. R. (1982). Nonparametric maximum likelihood estimation by the method of sieves. Annals of Statistics, 10(2), 401-414.
    • (1982) Annals of Statistics , vol.10 , Issue.2 , pp. 401-414
    • Geman, S.1    Gwang, C.R.2
  • 13
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    • Hierarchical mixtures of experts and the EM algorithm
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    • Jordan, M.I.1    Jacobs, R.A.2
  • 14
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    • Convergence results for the EM approach to mixtures of expert architectures
    • in press
    • Jordan, M. I., & Xu, L. (in press). Convergence results for the EM approach to mixtures of expert architectures. Neural Networks.
    • Neural Networks
    • Jordan, M.I.1    Xu, L.2
  • 17
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    • Universal approximation using radial-basis function networks
    • Park, J., & Sandberg, I. W. (1991). Universal approximation using radial-basis function networks. Neural Computation, 3, 246-257.
    • (1991) Neural Computation , vol.3 , pp. 246-257
    • Park, J.1    Sandberg, I.W.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.