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On Neural Network Architecture Based on Concept Lattices

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Abstract

Selecting an appropriate network architecture is a crucial problem when looking for a solution based on a neural network. If the number of neurons in network is too high, then it is likely to overfit. Neural networks also suffer from poor interpretability of learning results. In this paper an approach to building neural networks based on concept lattices and on lattices coming from monotone Galois connections is proposed in attempt to overcome the mentioned difficulties.

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Acknowledgments

The paper was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project 5-100.

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Authors and Affiliations

  1. Faculty of Computer Science, Department of Data Analysis and Artificial Intelligence, National Research University Higher School of Economics, Kochnovskiy pr. 3, Moscow, Russia

    Sergei O. Kuznetsov, Nurtas Makhazhanov & Maxim Ushakov

Authors
  1. Sergei O. Kuznetsov
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  2. Nurtas Makhazhanov
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  3. Maxim Ushakov
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Corresponding author

Correspondence to Nurtas Makhazhanov .

Editor information

Editors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Marzena Kryszkiewicz

  2. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  3. Institute of Informatics, University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  4. Faculty of Electronics & Information, Warsaw University of Technology, Warsaw, Poland

    Henryk Rybinski

  5. Institute of Mathematics, Warsaw University, Warsaw, Poland

    Andrzej Skowron

  6. Department of Computer Science, University of North Carolina at Charlotte, North Carolina, USA

    Zbigniew W. Raś

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Kuznetsov, S.O., Makhazhanov, N., Ushakov, M. (2017). On Neural Network Architecture Based on Concept Lattices. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_64

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  • DOI: https://doi.org/10.1007/978-3-319-60438-1_64

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60437-4

  • Online ISBN: 978-3-319-60438-1

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