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Volumn 3, Issue 3-4, 2008, Pages 221-230

Approximation capabilities of neural networks

Author keywords

Approximation; Learning; Neural networks

Indexed keywords


EID: 70350524319     PISSN: 17908140     EISSN: 17908159     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (11)

References (23)
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    • Ph.D. Thesis, Babes-Bolyai University, Cluj-Napoca
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  • 7
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    • On the approximate realization of continuous mappings by neural networks
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    • Funahashi, K.1
  • 8
    • 0024991997 scopus 로고
    • Networks and the Best Approximation Property
    • F. Girosi, T. Pogio, Networks and the Best Approximation Property. Biological Cybernetics, 63, 169-176 (1990).
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    • Girosi, F.1    Pogio, T.2
  • 13
    • 0024880831 scopus 로고
    • Multilayer Feedforward Networks Are Universal Approximators
    • K. Hornik, M. Stinchcombe, H. White, Multilayer Feedforward Networks Are Universal Approximators. Neural Networks, 2, 359-366 (1989).
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 16
    • 0001321138 scopus 로고
    • On the Representation of Continuous Functions of Many Variables by Superposition of Continuous Functions of One Variable and Addition
    • A. Kolmogorov, On the Representation of Continuous Functions of Many Variables by Superposition of Continuous Functions of One Variable and Addition. Doglady Akademii Nauk SSSR, 144, 679-681 (1957).
    • (1957) Doglady Akademii Nauk SSSR , vol.144 , pp. 679-681
    • Kolmogorov, A.1
  • 17
    • 0025857963 scopus 로고
    • Arbitrary Nonlinearity Is Sufficient to Represent All Functions by Neural Networks: A Theorem
    • V.Y. Kreinovich, Arbitrary Nonlinearity Is Sufficient to Represent All Functions by Neural Networks: A Theorem. Neural Networks, 4, 381-383 (1991).
    • (1991) Neural Networks , vol.4 , pp. 381-383
    • Kreinovich, V.Y.1
  • 18
    • 18144423627 scopus 로고    scopus 로고
    • Learning from Data as an Inverse Problem
    • (J. Antoch Ed.), Heidelberg: Phisica-Verlag / Springer
    • V. Kurkova, Learning from Data as an Inverse Problem. In COMPSTAT 2004 – Proceedings on Computer statistics (J. Antoch Ed.), 1377-1384, Heidelberg: Phisica-Verlag / Springer (2004).
    • (2004) COMPSTAT 2004 – Proceedings on Computer Statistics , pp. 1377-1384
    • Kurkova, V.1
  • 19
    • 33646049202 scopus 로고    scopus 로고
    • Research Report ICS-2004-960, Institute of Computer Science, Prague
    • V. Kurkova, Supervised Learning as an Inverse Problem. Research Report ICS-2004-960, Institute of Computer Science, Prague (2004).
    • (2004) Supervised Learning as an Inverse Problem
    • Kurkova, V.1
  • 20
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    • Interpolation of scattered data: Distance matrices and conditionally positive definite functions
    • C.A. Micchelli, Interpolation of scattered data: Distance matrices and conditionally positive definite functions. Constr. Approx., Vol. 2, 11-22 (1986).
    • (1986) Constr. Approx. , vol.2 , pp. 11-22
    • Micchelli, C.A.1
  • 21
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    • Networks for Approximation and Learning
    • T. Pogio, F. Girosi, Networks for Approximation and Learning. Proceedings of the IEEE, Vol. 78, 9, 1481-1497, Sept. (1990).
    • (1990) Proceedings of the IEEE , vol.78 , Issue.9 , pp. 1481-1497
    • Pogio, T.1    Girosi, F.2
  • 23
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    • Solution of incorrectly formulated problems and regularization method
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    • Tikhonov, A.N.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.