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Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and RecognitionTIMCHENKO, L. , KOKRIATSKAIA, N. , MELNIKOV, V. , MAKARENKO, R. , PETROVSKYI, N.
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parallel-hierarchical network, training, population coding, preparation, face recognition
timchenko(6), hierarchical(6), processing(5), recognition(4), parallel(4), neural(4), networks(4), network(4), learning(4), analysis(4)
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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
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|
| 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 10582]
 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]
 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]
 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]
 J. Sacramento, A. Wichert. Tree-like hierarchical associative memory structures. Journal Neural Networks, pp. 143-147, 2010. [PubMed]
 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]
 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.
 D. E Hinton. How do neural networks train? In the world of science, 11, 1992.
 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 1277] [SCOPUS Times Cited 1687]
 T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning. Springer, 2001.
 S. Gadat, L. Younes. A stochastic algorithm for feature selection in pattern recognition. Research Journal of Machine Learning Research (8), pp. 509-547, 2007.
 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.
 Manchester base of human faces. [Online] Available: Temporary on-line reference link removed - see the PDF document
 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 28]
 Tom Mitchel. Machine Learning . McGraw Hill, 432p, 1997.
 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.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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