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Volumn , Issue , 2014, Pages 371-376

On the complexity of shallow and deep neural network classifiers

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; NETWORK ARCHITECTURE; NEURAL NETWORKS;

EID: 84962018700     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (43)

References (17)
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    • 84904743910 scopus 로고    scopus 로고
    • On the complexity of neural network classifiers: A comparison between shallow and deep architectures
    • To be published
    • M. Bianchini and F. Scarselli. On the complexity of neural network classifiers: A comparison between shallow and deep architectures. IEEE Transactions on Neural Networks, 2014. To be published.
    • (2014) IEEE Transactions on Neural Networks
    • Bianchini, M.1    Scarselli, F.2
  • 9
    • 0345195977 scopus 로고    scopus 로고
    • Universal approximation using feedforward neural networks: A survey of some existing methods, and some new results
    • F. Scarselli and A.C. Tsoi. Universal approximation using feedforward neural networks: A survey of some existing methods, and some new results. Neural Networks, 11:15-37, 1998.
    • (1998) Neural Networks , vol.11 , pp. 15-37
    • Scarselli, F.1    Tsoi, A.C.2
  • 10
    • 0034069365 scopus 로고    scopus 로고
    • On the approximation capability of recurrent neural networks
    • B. Hammer. On the approximation capability of recurrent neural networks. Neurocom- puting, 31(1-4):107-123, 2000.
    • (2000) Neurocomputing , vol.31 , Issue.1-4 , pp. 107-123
    • Hammer, B.1
  • 11
    • 26944454812 scopus 로고    scopus 로고
    • Recursive neural networks for processing graphs with labelled edges: Theory and applications
    • M. Bianchini, M. Maggini, L. Sarti, and F. Scarselli. Recursive neural networks for processing graphs with labelled edges: Theory and applications. Neural Networks, 18(8):1040-1050, 2005.
    • (2005) Neural Networks , vol.18 , Issue.8 , pp. 1040-1050
    • Bianchini, M.1    Maggini, M.2    Sarti, L.3    Scarselli, F.4
  • 13
    • 0032096332 scopus 로고    scopus 로고
    • Representations and rates of approximation of real-valued boolean functions by neural networks
    • V. Kurkova, P. Savicky, and K. Hlavackova. Representations and rates of approximation of real-valued boolean functions by neural networks. Neural Networks, 11(4):651-659, 1998.
    • (1998) Neural Networks , vol.11 , Issue.4 , pp. 651-659
    • Kurkova, V.1    Savicky, P.2    Hlavackova, K.3
  • 15
    • 0001295178 scopus 로고
    • On the power of small-depth threshold circuits
    • J. Håstad and M. Goldmann. On the power of small-depth threshold circuits. Computational Complexity, 1(2):113-129, 1991.
    • (1991) Computational Complexity , vol.1 , Issue.2 , pp. 113-129
    • Håstad, J.1    Goldmann, M.2
  • 16
    • 4644327403 scopus 로고    scopus 로고
    • Vapnik-Chervonenkis dimension of neural nets
    • M.A. Arbib, editor. Cambridge, MA: MIT Press. Second Edition
    • P.L. Bartlett and W. Maass. Vapnik-Chervonenkis dimension of neural nets. In M.A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 1188-1192. Cambridge, MA: MIT Press, 2003. Second Edition.
    • (2003) The Handbook of Brain Theory and Neural Networks , pp. 1188-1192
    • Bartlett, P.L.1    Maass, W.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.