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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2012: Artificial Intelligence and Soft Computing pp 111–118Cite as

Binary Perceptron Learning Algorithm Using Simplex-Method

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7267))

Abstract

A number of researchers headed by E. Gardner have proved that a maximum achievable memory load of binary perceptron is 2. A learning algorithm allowing reaching and even exceeding the critical load was proposed. The algorithm was reduced to solving the linear programming problem. The proposed algorithm is sequel to Krauth and Mezard ideas. The algorithm makes it possible to construct networks storage capacity and noise stability of which are comparable to those of Krauth and Mezard algorithm. However suggested modification of the algorithm outperforms.

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References

  1. Karandashev, I., Kryzhanovsky, B., Litinskii, L.: Hopfield-type memory without catastrophic forgetting. In: Future Computing 2011: The Third International Conference on Future Computational Technologies and Applications, pp. 57–61. IARIA (2011)

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  4. Kryzhanovsky, B.V., Kryzhanovsky, V.M.: Binary Optimization: On the Probability of a Local Minimum Detection in Random Search. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 89–100. Springer, Heidelberg (2008)

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  5. Krauth, W., Mezard, M.: Learning algorithms with optimal stability in neural networks. J. Phys. A: Math. Gen. 20, L745–L752 (1987)

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Author information

Authors and Affiliations

  1. Center of Optical Neural Technologies, Scientific Research Institute for System Analysis, Russian Academy of Sciences, Vavilova St., 44/2, 119333, Moscow, Russia

    Vladimir Kryzhanovskiy, Irina Zhelavskaya & Jakov Karandashev

Authors
  1. Vladimir Kryzhanovskiy
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  2. Irina Zhelavskaya
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  3. Jakov Karandashev
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Kryzhanovskiy, V., Zhelavskaya, I., Karandashev, J. (2012). Binary Perceptron Learning Algorithm Using Simplex-Method. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_13

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  • DOI: https://doi.org/10.1007/978-3-642-29347-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29346-7

  • Online ISBN: 978-3-642-29347-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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