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Binary Patterns Identification by Vector Neural Network with Measure of Proximity between Neuron States

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Artificial Neural Networks and Machine Learning – ICANN 2011 (ICANN 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6792))

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Abstract

I describe a new vector neural network, in which a priori information about the distribution of noise is easily and naturally embedded. Taking into account the noise distribution allows to essentially increase the system noise immunity. A measure of proximity between neuron states is embedded for the first time. It makes possible to use the prior information. On binary identification problem the one order increase of storage capacity is shown.

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Author information

Authors and Affiliations

  1. Scientific Research Institute for System Analysis of Russian Academy of Sciences, Moscow, Russia

    Vladimir Kryzhanovskiy

Authors
  1. Vladimir Kryzhanovskiy
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Editor information

Editors and Affiliations

  1. Department of Information and Computer Science, Aalto University School of Science, P.O. Box 15400, 00076, Aalto, Finland

    Timo Honkela  & Samuel Kaski  & 

  2. School of Physics, Astronomy and Informatics, Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  3. Department of Statistical Science, University College London, 1-19 Torrington Place, WC1E 7HB, London, UK

    Mark Girolami

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Kryzhanovskiy, V. (2011). Binary Patterns Identification by Vector Neural Network with Measure of Proximity between Neuron States. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_16

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  • DOI: https://doi.org/10.1007/978-3-642-21738-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21737-1

  • Online ISBN: 978-3-642-21738-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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