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Performance criterion of neural networks learning

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Abstract

The article deals with a new criterion (the wave criterion) of estimating the generalizing ability of a neural network. The theoretical grounding of the criterion is based on Bayes theorem, the methods of cybernetics and synergy. The computing experiments carried out with the databases Fmtrain, Matt, Grng, Ada have proved the criterion to be an effective tool.

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References

  1. Lawrence, S., Lee, C., and Tsoi, C., Lessons in Neural Network Training: Overfitting May be Harder than Expected, Proceedings of the Fourteenth National Conference on Artificial Intelligence, AAAI-97 (Menlo Park, 1997), California: AAAI Press, pp. 540–545.

    Google Scholar 

  2. Anderson, D., Burnham, K., and Thompson, W., Null Hypothesis Testing: Problems, Prevalence, and an Alternative, Journal of Wildlife Management, 2000, vol. 64, no. 4, pp. 912–923.

    Article  Google Scholar 

  3. Vapnik, V., The Nature of Statistical Learning Theory, Springer, 1995.

  4. Akaike, H., Information Theory and an Extension of the Maximum Likelihood Principle, Proceeding of 2nd International Symposium on Information Theory Budapest: Akademia Kiado, 1973, pp. 267–281.

    Google Scholar 

  5. Rissanen, J., Modeling by Shortest Data Description, Automatica, 1978, vol. 14, pp. 465–471.

    Article  MATH  Google Scholar 

  6. Prigogine, I.R., Arising from the Existing to: the Time and Complexity in the Natural Sciences, M.: University Press, 1985.

    Google Scholar 

  7. Potapov, A.S., Recognition of Images and Machine Perception: The General Approach Based on the Principle of Minimum Length Description, St. Petersburg: Polytechnique, 2007.

    Google Scholar 

  8. Gorban, A.N., Dunin-Barkowski, V.L., Kirdin, A.N., et al., Neuroinformatics, Novosibirsk: Science. Siberian Enterprise RAS, 1998.

    Google Scholar 

  9. Besekersky, V.A. and Popov, E.P., The Theory of Automatic Adjustment, M.: University Press, 1975.

    Google Scholar 

  10. Sekerin, A.B., The Method of Assessing the Sustainability Neyronno—Network Models./2005. http://zhurnal.ape.relarn.ru/articles/2005/031.pdf.

  11. Kobozev, N.I., Research for the Thermodynamics of Information and Thinking, Moscow: Moscow State University, 1971.

    Google Scholar 

  12. Beloglazov, V.V. and Biryuk, N.D., Analysis of the Stability of Parametric Method of Lyapunov Second Circuit, Voronezh. VSU Bulletin, Series Physics, Mathematics, 2003, no. 1.

  13. Sibert, U.M., Chains Signals System, Moscow: Mir, 1988.

    Google Scholar 

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  1. CJSC “Infozentr”, 655017, Abakan, Pushkina street 199b, Russia, Republic of Khakassia

    A. A. Larko

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  1. A. A. Larko
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Correspondence to A. A. Larko.

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Larko, A.A. Performance criterion of neural networks learning. Opt. Mem. Neural Networks 17, 208–219 (2008). https://doi.org/10.3103/S1060992X08030041

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  • DOI: https://doi.org/10.3103/S1060992X08030041

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