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Volumn 122, Issue 2, 2003, Pages 260-266

Approximation by neural networks with a bounded number of nodes at each level

Author keywords

Approximation; Multilayer; Network; Neural

Indexed keywords


EID: 0038686384     PISSN: 00219045     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0021-9045(03)00078-9     Document Type: Article
Times cited : (41)

References (10)
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  • 2
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  • 3
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    • Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
    • M. Leshno, V.Y. Lin, A. Pinkus, S. Schocken, Multilayer feedforward networks with a nonpolynomial activation function can approximate any function, Neural Networks 6 (1993) 861-867.
    • (1993) Neural Networks , vol.6 , pp. 861-867
    • Leshno, M.1    Lin, V.Y.2    Pinkus, A.3    Schocken, S.4
  • 4
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    • Constructive Approximation, Advanced Problems
    • Springer, Berlin
    • G.G. Lorentz, M.V. Golitschek, Y. Makovoz, Constructive Approximation, Advanced Problems, Springer, Berlin, 1996.
    • (1996)
    • Lorentz, G.G.1    Golitschek, M.V.2    Makovoz, Y.3
  • 5
    • 0032950772 scopus 로고    scopus 로고
    • Lower bounds for approximation by MLP neural networks
    • V. Maiorov, A. Pinkus, Lower bounds for approximation by MLP neural networks, Neurocomputing 25 (1-3) (1999) 81-91.
    • (1999) Neurocomputing , vol.25 , Issue.1-3 , pp. 81-91
    • Maiorov, V.1    Pinkus, A.2
  • 6
    • 0001574595 scopus 로고    scopus 로고
    • Uniform approximation by neural networks
    • Y. Makovoz, Uniform approximation by neural networks, J. Approx. Theory 95 (2) (1998) 215-228.
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    • Makovoz, Y.1
  • 7
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    • Approximation theory of the MLP model in neural networks
    • Cambridge University Press, Cambridge
    • A. Pinkus, Approximation theory of the MLP model in neural networks, in: Acta Numerica, Vol. 8, Cambridge University Press, Cambridge, 1999, pp. 143-195.
    • (1999) Acta Numerica , vol.8 , pp. 143-195
    • Pinkus, A.1
  • 8
    • 0345195977 scopus 로고    scopus 로고
    • Universal approximation using feedforward neural networks: A survey of some existing methods and some new results
    • F. Scarselli, A.C. Tsoi, Universal approximation using feedforward neural networks: a survey of some existing methods and some new results, Neural Networks 11 (1998) 15-37.
    • (1998) Neural Networks , vol.11 , pp. 15-37
    • Scarselli, F.1    Tsoi, A.C.2
  • 9
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    • Capabilities of four-layered feedforward neural network: Four layers versus three
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    • (1997) IEEE Trans. Neural Networks , vol.8 , Issue.2 , pp. 251-255
    • Tamura, S.1    Tateish, M.2
  • 10
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    • Backpropagation neural nets with one and two hidden layers
    • J. de Villiers, D. Barnard, Backpropagation neural nets with one and two hidden layers, IEEE Trans. Neural Networks 4 (12) (1992) 136-141.
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    • de Villiers, J.1    Barnard, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.