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Volumn , Issue , 2014, Pages 2935-2939

An improved RBM based on Bayesian Regularization

Author keywords

classification; over fitting; regularization; Restricted Boltzmann Machine

Indexed keywords

CLASSIFICATION (OF INFORMATION); DEEP LEARNING;

EID: 84908475951     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2014.6889458     Document Type: Conference Paper
Times cited : (13)

References (19)
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  • 3
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  • 4
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    • Hinton, G.E.1
  • 5
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • Hinton, G. E., Osindero, S. and Teh, Y, "A fast learning algorithm for deep belief nets". Neural Computation, 18:1527-1554, 2006
    • (2006) Neural Computation , vol.18 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.3
  • 8
    • 45749110924 scopus 로고    scopus 로고
    • Representational power of restricted Boltzmann machines and deep belief networks
    • Roux N L, Bengio Y. "Representational power of restricted Boltzmann machines and deep belief networks". Neural Computation, 20(6):1631-1649, 2006.
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    • Roux, N.L.1    Bengio, Y.2
  • 14
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    • Empirical analysis of the divergence of Gibbs sampling based learning algorithms for restricted Boltzmann machine
    • Berlin, Springer-Verlag
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    • (2010) Proceedings of the 20th International Conference on Artificial Neural Networks, Part 3 , vol.6354 LNCS , pp. 208-217
    • Fischer, A.1    Igel, C.2
  • 17
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    • Iterative choices of regularization parameter in linear inverse problems
    • Kunisch K., Zou J. "Iterative Choices of Regularization Parameter in Linear Inverse Problems". Inverse Problems, 14:1247-1264, 1998.
    • (1998) Inverse Problems , vol.14 , pp. 1247-1264
    • Kunisch, K.1    Zou, J.2
  • 19
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.