|4/2012 - 6|
Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and RecognitionTIMCHENKO, L. , KOKRIATSKAIA, N. , MELNIKOV, V. , MAKARENKO, R. , PETROVSKYI, N.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (718 KB) | Citation | Downloads: 425 | Views: 2,449|
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)
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
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 7552]
 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 16] [SCOPUS Times Cited 21]
 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 1148] [SCOPUS Times Cited 1481]
 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 17] [SCOPUS Times Cited 27]
 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.
Web of Science® Citations for all references: 1,188 TCR
SCOPUS® Citations for all references: 9,100 TCR
Web of Science® Average Citations per reference: 70 ACR
SCOPUS® Average Citations per reference: 535 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 2021-01-14 17:30 in 54 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.