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Pattern Recognition on the Basis of Boltzmann Machine Model

  • Andrey Babynin
  • Leonid GladkovEmail author
  • Nadezhda Gladkova
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 464)

Abstract

In the article the actual problem of increasing the efficiency of solving the pattern recognition problem is considered. It is described a promising approach to solve this problem by the use of artificial neural networks. It is proposed the model of a neural network as the Boltzmann machine. As a neural network learning algorithm the authors propose to use a simulated annealing algorithm. The deep learning methods of neural networks are considered. The algorithm of neural network functioning based on the Boltzmann machine model is suggested. The authors describe possibilities of using multi-layer neural network models, such as the deep Boltzmann machines. Advantages and disadvantages of the proposed approaches were found out. To estimate the proposed method the authors carried out the comparison of the known test set of sample images (MNIST). The results confirm the effectiveness of the proposed approaches.

Keywords

ECE Design Elements placement Optimization Genetic algorithm Fuzzy logic 

Notes

Acknowledgment

This research is supported by grants of the Ministry of Education and Science of the Russian Federation, the project # 8.823.2014.

References

  1. 1.
    Nazarov, A.V., Loscutov, A.I.: Neural network algorithms for prediction and optimization. Science and Technology (2003)Google Scholar
  2. 2.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507 (2006)Google Scholar
  4. 4.
    Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for Boltzmann machines. Cogn. Sci. 9(1), 147–169 (1985)Google Scholar
  5. 5.
    Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. NECO, 14(8), 1771–1800 (2002)Google Scholar
  6. 6.
    Teh, Y.W., Hinton, G.E.: Rate-coded restricted Boltzmann machines for face recognition. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) Advances in Neural Information Processing Systems, 13, pp. 908–914. MIT Press, Cambridge, MA (2001)Google Scholar
  7. 7.
    Salakhutdinov, R., Hinton, G.E.: Deep Boltzmann machines.In: Proceedings of the International Conference on Artificial Intelligence and Statistics, vol. 5, pp. 448–455 (2009)Google Scholar
  8. 8.
    Montavon, G., M¨uller, K.R.: Learning feature hierarchies with centered deep Boltzmann machines. CoRR (2012). arXiv:1203.4416
  9. 9.
    Le Roux, N., Bengio, Y.: Representational power of restricted Boltzmann machines and deep belief networks. Neural Comput. 20(6), 1631–1649 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Hinton, G., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Kelly, J.D., Davis, L.A: Hybrid genetic algorithm for classification. In: Proceedings of 12th International Joint Conference on Artificial Intelligence. Sydney, Morgan Kaufmann. pp. 645–650 (1991)Google Scholar
  12. 12.
    Chen, M., Yao, Z.: Classification techniques of neural networks using improved genetic algorithms. In: Proceedings of 2nd International Conference on Genetic and Evolutionary Computing. Washington. pp. 115–119 (2008)Google Scholar
  13. 13.
    Vivekanandan, P., Nedunchezhian, R.: A new incremental genetic algorithm based classification model to mine data with concept drift. J. Inf. Technol. Theory Appl. 21, 36–42 (2010)Google Scholar
  14. 14.
    Rodriguez, M.A., Escalante, D.M., Peregrin, A.: Efficient distributed genetic algorithm for rule extraction. Appl. Soft Comput. 11, 733–743 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andrey Babynin
    • 1
  • Leonid Gladkov
    • 1
    Email author
  • Nadezhda Gladkova
    • 1
  1. 1.Southern Federal UniversityRostov-on-DonRussia

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