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Volumn 2, Issue 5, 1989, Pages 359-366

Multilayer feedforward networks are universal approximators

Author keywords

Back propagation networks; Feedforward networks; Mapping networks; Network representation capability; Sigma Pi networks; Squashing functions; Stone Weierstrass Theorem; Universal approximation

Indexed keywords

MATHEMATICAL TECHNIQUES--APPROXIMATION THEORY; MATHEMATICAL TRANSFORMATIONS;

EID: 0024880831     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/0893-6080(89)90020-8     Document Type: Article
Times cited : (16942)

References (26)
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    • Convergence rates of maximum likelihood and related estimates in general parameter spaces
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    • The case for conceptual and operational separation of network architectures and learning mechanisms
    • Department of Economics, University of California, San Diego, CA, San Diego
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    • Multilayer feedforward networks can learn arbitrary mappings: Connectionist nonparametric regression with automatic and semi-automatic determination of network complexity
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    • (1988) Discussion Paper
    • White1
  • 25
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    • White, H., & Wooldridge, J. M. (in press). Some results for sieve estimation with dependent observations. In W. Barnett, J. Powell, & G. Tauchen (Eds.), Nonparametric and semi-parametric methods in econometrics and statistic. New York: Cambridge University Press.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.