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Volumn 10, Issue 8, 1998, Pages 2115-2135

Density Estimation by Mixture Models with Smoothing Priors

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EID: 0343881259     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976698300016990     Document Type: Article
Times cited : (13)

References (16)
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    • Jordan, M.I.1    Xu, L.2
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    • A practical Bayesian framework for backprop networks
    • MacKay, D. J. C. (1992). A practical Bayesian framework for backprop networks. Neural Computation, 4, 448-472.
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    • MacKay, D.J.C.1
  • 11
    • 85156248415 scopus 로고    scopus 로고
    • Improved gaussian mixture density estimates using Bayesian penalty terms and networks averaging
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    • Ormoneit, D., & Tresp, V. (1996). Improved gaussian mixture density estimates using Bayesian penalty terms and networks averaging. In D. S. Touretzky, M. C. Mozer, M. E. Hasselmo (Eds.), Advances in neural information processing systems, 8 (pp. 542-548). Cambridge, MA: MIT Press.
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    • Ormoneit, D.1    Tresp, V.2
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  • 15
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    • Utsugi, A. (1997). Hyperparameter selection for self-organizing maps. Neural Computation, 9, 623-635.
    • (1997) Neural Computation , vol.9 , pp. 623-635
    • Utsugi, A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.