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Estimates of Network Complexity and Integral Representations

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Artificial Neural Networks - ICANN 2008 (ICANN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5163))

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Abstract

Upper bounds on rates of approximation by neural networks are derived for functions representable as integrals in the form of networks with infinitely many units. The bounds are applied to perceptron networks.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Georgetown University, Washington, D.C. 20057-1233, USA

    Paul C. Kainen

  2. Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou věží  2, Prague 8, Czech Republic

    Věra Kůrková

Authors
  1. Paul C. Kainen
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  2. Věra Kůrková
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Editor information

Véra Kůrková Roman Neruda Jan Koutník

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Kainen, P.C., Kůrková, V. (2008). Estimates of Network Complexity and Integral Representations. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_4

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  • DOI: https://doi.org/10.1007/978-3-540-87536-9_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87535-2

  • Online ISBN: 978-3-540-87536-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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