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Volumn , Issue , 2016, Pages 2066-2072

On the depth of deep neural networks: A theoretical view

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; ERRORS;

EID: 84999110931     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (144)

References (31)
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    • Bartlett, P.L.1    Mendelson, S.2
  • 2
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    • Almost linear vc-dimension bounds for piecewise polynomial networks
    • Bartlett, P. L.; Maiorov, V.; and Meir, R. 1998. Almost linear vc-dimension bounds for piecewise polynomial networks. Neural computation 10(8):2159-2173.
    • (1998) Neural Computation , vol.10 , Issue.8 , pp. 2159-2173
    • Bartlett, P.L.1    Maiorov, V.2    Meir, R.3
  • 3
    • 0032028728 scopus 로고    scopus 로고
    • The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network
    • Bartlett, P. L. 1998. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Transactions on Information Theory 44(2):525-536.
    • (1998) IEEE Transactions on Information Theory , vol.44 , Issue.2 , pp. 525-536
    • Bartlett, P.L.1
  • 4
    • 84904743910 scopus 로고    scopus 로고
    • On the complexity of neural network classifiers: A comparison between shallow and deep architectures
    • Bianchini, M., and Scarselli, F. 2014. On the complexity of neural network classifiers: A comparison between shallow and deep architectures. IEEE Transactions on Neural Networks.
    • (2014) IEEE Transactions on Neural Networks
    • Bianchini, M.1    Scarselli, F.2
  • 10
    • 0029256399 scopus 로고
    • Bounding the vapnik-chervonenkis dimension of concept classes parameterized by real numbers
    • Goldberg, P. W., and Jerrum, M. R. 1995. Bounding the vapnik-chervonenkis dimension of concept classes parameterized by real numbers. Machine Learning 18(2-3):131- 148.
    • (1995) Machine Learning , vol.18 , Issue.2-3 , pp. 131-148
    • Goldberg, P.W.1    Jerrum, M.R.2
  • 13
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton, G. E., and Salakhutdinov, R. R. 2006. Reducing the dimensionality of data with neural networks. Science 313(5786):504-507.
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 17
    • 0036104545 scopus 로고    scopus 로고
    • Empirical margin distributions and bounding the generalization error of combined classifiers
    • Koltchinskii, V., and Panchenko, D. 2002. Empirical margin distributions and bounding the generalization error of combined classifiers. Annals of Statistics 1-50.
    • (2002) Annals of Statistics , pp. 1-50
    • Koltchinskii, V.1    Panchenko, D.2
  • 20
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11):2278-2324.
    • (1998) Proceedings of the IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • LeCun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 31
    • 0033145513 scopus 로고    scopus 로고
    • Betti numbers of semi-pfaffian sets
    • Zell, T. 1999. Betti numbers of semi-pfaffian sets. Journal of Pure and Applied Algebra 139(1):323-338.
    • (1999) Journal of Pure and Applied Algebra , vol.139 , Issue.1 , pp. 323-338
    • Zell, T.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.