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Machine Learning in Vector Models of Neural Networks

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Book cover Advances in Machine Learning II

Part of the book series: Studies in Computational Intelligence ((SCI,volume 263))

Abstract

We present the review of our works related to the theory of vector neural networks. The interconnection matrix always is constructed according to the generalized Hebb’s rule, which is well-known in the Machine Learning. We accentuate the main principles and ideas. Analytical calculations are based on the probability approach. The obtained theoretical results are verified with the aid of computer simulations.

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Author information

Authors and Affiliations

  1. Center of Optical Neural Technologies of Scientific Research Institute for System Analysis of Russian Academy of Sciences, Moscow

    Boris Kryzhanovsky, Vladimir Kryzhanovsky & Leonid Litinskii

Authors
  1. Boris Kryzhanovsky
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  2. Vladimir Kryzhanovsky
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  3. Leonid Litinskii
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Editor information

Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, ul. Ordona 21, 01-237, Warsaw, Poland

    Jacek Koronacki  & Sławomir T. Wierzchoń  & 

  2. Woodward Hall 430C, University of North Carolina, 9201 University City Blvd., N.C. 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Systems Research Institute, Polish Academy of Sciences, ul.Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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Kryzhanovsky, B., Kryzhanovsky, V., Litinskii, L. (2010). Machine Learning in Vector Models of Neural Networks. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning II. Studies in Computational Intelligence, vol 263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05179-1_20

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  • DOI: https://doi.org/10.1007/978-3-642-05179-1_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05178-4

  • Online ISBN: 978-3-642-05179-1

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