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Approximation of Binary-Valued Functions by Networks of Finite VC Dimension

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Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

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Abstract

Distributions of errors in approximation of binary-valued functions by networks with sets of input-output functions of finite VC dimension is investigated. Conditions on concentration of approximation errors around their mean values are derived in terms of growth functions of sets of input-output functions. Limitations of approximation capabilities of networks of finite VC dimension are discussed.

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Acknowledgments

This work was partially supported by the Czech Science Foundation grant 22-02067S and the institutional support of the Institute of Computer Science RVO 67985807.

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Authors and Affiliations

  1. Institute of Computer Science of the Czech Academy of Sciences, Pod Vodárenskou věží  2, 18207, Prague, Czech Republic

    Věra Kůrková

Authors
  1. Věra Kůrková
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Correspondence to Věra Kůrková .

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Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  3. Lancaster University, Lancaster, UK

    Plamen Angelov

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

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Kůrková, V. (2023). Approximation of Binary-Valued Functions by Networks of Finite VC Dimension. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14254. Springer, Cham. https://doi.org/10.1007/978-3-031-44207-0_40

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  • DOI: https://doi.org/10.1007/978-3-031-44207-0_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44206-3

  • Online ISBN: 978-3-031-44207-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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