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Volumn 12, Issue 3, 2000, Pages 277-290

Bayesian sampling and ensemble learning in generative topographic mapping

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; GIBBS FREE ENERGY; LAPLACE TRANSFORMS; LEARNING ALGORITHMS; MATHEMATICAL MODELS; STATISTICAL METHODS;

EID: 0034504355     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1026567325853     Document Type: Article
Times cited : (10)

References (16)
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  • 4
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    • Statistical mechanics as the underlying theory of 'elastic' and 'neural' optimisations
    • Simic, P. D.: 1990, Statistical mechanics as the underlying theory of 'elastic' and 'neural' optimisations. Network, 1,89-103.
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    • Simic, P.D.1
  • 5
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    • A dimension reduction framework for understanding cortical maps
    • Durbin, R. and Mitchison, G.: 1990, A dimension reduction framework for understanding cortical maps. Nature, 343, 644-647.
    • (1990) Nature , vol.343 , pp. 644-647
    • Durbin, R.1    Mitchison, G.2
  • 6
    • 0004565770 scopus 로고    scopus 로고
    • Topology selection for self-organizing maps
    • Utsugi, A.: 1996, Topology selection for self-organizing maps. Network, 7, 727-740.
    • (1996) Network , vol.7 , pp. 727-740
    • Utsugi, A.1
  • 7
    • 0005023590 scopus 로고    scopus 로고
    • Hyperparameter selection for self-organizing maps
    • 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
  • 8
    • 0343881259 scopus 로고    scopus 로고
    • Density estimation by mixture models with smoothing priors
    • Utsugi, A.: 1998, Density estimation by mixture models with smoothing priors. Neural Computation, 10, 2115-2135.
    • (1998) Neural Computation , vol.10 , pp. 2115-2135
    • Utsugi, A.1
  • 10
    • 0344110441 scopus 로고    scopus 로고
    • Developments of the generative topographic mapping
    • Bishop, C.M., Svensén, M. and Williams, C.K.I.: 1998, Developments of the generative topographic mapping. Neurocomputing, 21, 203-224.
    • (1998) Neurocomputing , vol.21 , pp. 203-224
    • Bishop, C.M.1    Svensén, M.2    Williams, C.K.I.3
  • 11
    • 0002704818 scopus 로고
    • 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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  • 14
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    • Bayesian methods for mixtures of experts
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    • Waterhouse, S., MacKay, D. and Robinson, T.: 1996, Bayesian methods for mixtures of experts. In: D.S. Touretzky, M.C. Mozer and M.E. Haaslmo, eds, Advances in Neural Information Processing Systems 8, MIT Press, Cambridge, pp. 351-357.
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    • Waterhouse, S.1    MacKay, D.2    Robinson, T.3
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    • Comparison of approximate methods for handling hyperparameters
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.