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Volumn 4432 LNCS, Issue PART 2, 2007, Pages 11-18

Estimates of approximation rates by gaussian radial-basis functions

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; GAUSSIAN DISTRIBUTION; PARAMETER ESTIMATION;

EID: 38049029310     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71629-7_2     Document Type: Conference Paper
Times cited : (9)

References (22)
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    • Layered neural networks with Gaussian hidden units as universal approximations
    • Hartman, E. J., Keeler, J. D., Kowalski, J. M.: Layered neural networks with Gaussian hidden units as universal approximations. Neural Computation 2 (1990) 210-215
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    • Hartman, E.J.1    Keeler, J.D.2    Kowalski, J.M.3
  • 8
    • 0000796112 scopus 로고
    • A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training
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    • Rates of approximation of smooth functions by Gaussian radial-basis- function networks
    • ICS-976
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    • (2006) Research report
    • Kainen, P.C.1    Kůrková, V.2    Sanguineti, M.3
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    • Extending Girosi's approximation estimates for functions in Sobolev spaces via statistical learning theory
    • Kon, M. A., Raphael, L. A., Williams, D. A.: Extending Girosi's approximation estimates for functions in Sobolev spaces via statistical learning theory. J. of Analysis and Applications 3 (2005) 67-90
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    • Kon, M.A.1    Raphael, L.A.2    Williams, D.A.3
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    • Approximating functions in reproducing kernel Hilbert spaces via statistical learning theory
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