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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2012 - 6

Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and Recognition

TIMCHENKO, L. See more information about TIMCHENKO, L. on SCOPUS See more information about TIMCHENKO, L. on IEEExplore See more information about TIMCHENKO, L. on Web of Science, KOKRIATSKAIA, N. See more information about  KOKRIATSKAIA, N. on SCOPUS See more information about  KOKRIATSKAIA, N. on SCOPUS See more information about KOKRIATSKAIA, N. on Web of Science, MELNIKOV, V. See more information about  MELNIKOV, V. on SCOPUS See more information about  MELNIKOV, V. on SCOPUS See more information about MELNIKOV, V. on Web of Science, MAKARENKO, R. See more information about  MAKARENKO, R. on SCOPUS See more information about  MAKARENKO, R. on SCOPUS See more information about MAKARENKO, R. on Web of Science, PETROVSKYI, N. See more information about PETROVSKYI, N. on SCOPUS See more information about PETROVSKYI, N. on SCOPUS See more information about PETROVSKYI, N. on Web of Science
 
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Download PDF pdficon (718 KB) | Citation | Downloads: 143 | Views: 2,988

Author keywords
parallel-hierarchical network, training, population coding, preparation, face recognition

References keywords
timchenko(6), hierarchical(6), processing(5), recognition(4), parallel(4), neural(4), networks(4), network(4), learning(4), analysis(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-11-30
Volume 12, Issue 4, Year 2012, On page(s): 39 - 46
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.04006
Web of Science Accession Number: 000312128400006
SCOPUS ID: 84872764925

Abstract
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Propositions necessary for development of parallel-hierarchical (PH) network training methods are discussed in this article. Unlike already known structures of the artificial neural network, where non-normalized (absolute) similarity criteria are used for comparison, the suggested structure uses a normalized criterion. Based on the analysis of training rules, a conclusion is made that application of two training methods with a teacher is optimal for PH network training: error correction-based training and memory-based training. Mathematical models of training and a combined method of PH network training for recognition of static and dynamic patterns are developed.


References | Cited By  «-- Click to see who has cited this paper

[1] W. S. McCulloch and W. Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, Vol. 5, pp. 115-133, 1943.
[CrossRef] [SCOPUS Times Cited 9051]


[2] L. I. Timchenko, V. V. Melnikov, N.I. Kokryatskaya, Yu. F. Kutaev, I.D. Ivasyuk. A method of organization of a parallel-hierarchical network for image recognition. Journal Cybernetics and system analysis. , Vol.47 (1), pp. 140-151, 2011.
[CrossRef] [SCOPUS Times Cited 7]


[3] L. I. Timchenko, V. V. Melnikov, N.I. Kodryatskaya, Parallel-hierarchical network learning methods and their application to pattern recognition, Cybernetics and Systems Analysis, 47(6), 2011.
[CrossRef] [SCOPUS Times Cited 2]


[4] M. Hirahara, N. Oka, T. Kindo. A cascade associative memory model with a hierarchical memory structure. Journal Neural Networks, Vol.13, Issue 1, pp. 41-50, 2000.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 10]


[5] J. Sacramento, A. Wichert. Tree-like hierarchical associative memory structures. Journal Neural Networks, pp. 143-147, 2010. [PubMed]

[6] L. I. Timchenko. A multistage parallel-hierarchic network as a model of a neurolike computation scheme. Journal Cybernetics and system analysis. - Vol.36(2), pp. 251-267, 2000.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 23]


[7] L. I. Timchenko, Y. F. Kutaev, S.V. Chepornyuk, M.A. Grudin, A.A. Gertsiy.A brain-like approach to multistage hierarchical image processing. Springer-Verlag Processing. - in Proc. Image Analysis and Processing, Florence, Italy, pp. 246 - 253, 1997.

[8] D. E Hinton. How do neural networks train? In the world of science, 11, 1992.

[9] B. Widrow, and M. A. Lehr. 30 years of adaptive neural networks: Perceptron, madaline and backpropagation. Proceedings of the Institute of Electrical and Electronics Engineers, Vol. 78, p. 1415-1442, 1990.
[CrossRef] [Web of Science Times Cited 1234] [SCOPUS Times Cited 1609]


[10] T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning. Springer, 2001.

[11] S. Gadat, L. Younes. A stochastic algorithm for feature selection in pattern recognition. Research Journal of Machine Learning Research (8), pp. 509-547, 2007.

[12] L. I. Timchenko, N. I. Kokryatskaya, A.A. Poplavskyy, A.A Poplavska, I.D. Ivasyuk. Method of reference tunnel formation for improvement of forecast results of laser beams spot images behavior. 18th International Conference IWSSIP-2011, pp. 1-3., 2011b.

[13] Manchester base of human faces. [Online] Available: Temporary on-line reference link removed - see the PDF document

[14] L. I. Timchenko, Y. F. Kutaev, V. P. Kozhemyako, et al. Method for Training of a Parallel-Hierarchical Network, Based on Population Coding for Processing of Extended Laser Paths Images. Proceedings of SPIE, Vol. 4790, pp. 465-479, 2002.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 27]


[15] Tom Mitchel. Machine Learning . McGraw Hill, 432p, 1997.

[16] V. P. Kozhemyako, E. I. Ponuraya, V. Belokonniy. Logic-temporal functions processing for object recognition. Selected papers from the International Conference on Optoelectronic Information Technologies. Bellingham, Wash., USA, SPIE,+ Vol.4425, pp. 35-40, 2001.



References Weight

Web of Science® Citations for all references: 1,276 TCR
SCOPUS® Citations for all references: 10,729 TCR

Web of Science® Average Citations per reference: 75 ACR
SCOPUS® Average Citations per reference: 631 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-06-29 08:25 in 46 seconds.




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Faculty of Electrical Engineering and Computer Science
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