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International Conference on Engineering Applications of Neural Networks

EANN 2021: Proceedings of the 22nd Engineering Applications of Neural Networks Conference pp 436–447Cite as

Repeatable Functionalities in Complex Layers of Formal Neurons

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Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 3))

Abstract

Complex layers of formal neurons can be designed on data sets built from a relatively small number of multidimensional feature vectors. Data sets with this structure can almost always be separated linearly. Genetic data sets typically have this property.

Maximizing the margins is a fundamental principle when designing linear classifiers (formal neurons) based on training sets consisting of a small number of multivariate feature vectors. Maximizing Euclidean (L2) margins is a basic concept in the support vector machines (SVM) method of classifiers designing. An alternative approach to designing formal neurons may be to maximize the margins based on the L1 norm. The margins of the L1 norm enable the design of complex layers of formal neurons.

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Acknowledgments

The presented study was supported by the grant WZ/WI-IIT/3/2020 from the Bialystok University of Technology and funded from the resources for research by the Polish Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, Bialystok, Poland

    Leon Bobrowski & Tomasz Łukaszuk

  2. Institute of Biocybernetics and Biomedical Engineering, PAS, Warsaw, Poland

    Leon Bobrowski

Authors
  1. Leon Bobrowski
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  2. Tomasz Łukaszuk
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Corresponding author

Correspondence to Leon Bobrowski .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Faculty of Applied Sciences, University of Sunderland, Sunderland, UK

    John Macintyre

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Bobrowski, L., Łukaszuk, T. (2021). Repeatable Functionalities in Complex Layers of Formal Neurons. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_36

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