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A Study on Enhanced Self-Generation Supervised Learning Algorithm for Image Recognition  

Kim, Tae-Kyung (중앙대학교 첨단영상대학원 영상공학과 시각및지능시스템연구실)
Kim, Kwang-Baek (신라대학교 공과대학 컴퓨터공학과)
Paik, Joon-Ki (중앙대학교 첨단영상대학원 영상공학과 시각및지능시스템연구실)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.30, no.2C, 2005 , pp. 31-40 More about this Journal
Abstract
we propose an enhanced self-generation supervised algorithm that by combining an ART algorithm and the delta-bar-delta method. Form the input layer to the hidden layer, ART-1 and ART-2 are used to produce nodes, respectively. A winner-take-all method is adopted to the connection weight adaption so that a stored pattern for some pattern is updated. we test the recognition of student identification, a certificate of residence, and an identifier from container that require nodes of hidden layers in neural network. In simulation results, the proposed self-generation supervised learning algorithm reduces the possibility of local minima and improves learning speed and paralysis than conventional neural networks.
Keywords
Delta-bar-delta; ARI-1; ART-2; Self-generation Supervised Learning Algorithm;
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1 Rumelhart, D. E. McClelland, J. L. and the PDP Reserch Group(Eds), parallel distributed processing, vol. 1, pp. 318-536, Cambridge, MA: MIT Press
2 James A. Freeman, David M. Skapura, Neural Networks Algorithms, applications, and Programming Techiques, addison Wesley, pp. 89-125, 1991
3 R. Allen, 'Several Studies on Natural Language and Back Propagation,' IEEE ICNN, vol. II, pp. 335-342, 1987
4 Y. H. Ju, T. K. Kim, K. B. Kim, 'A Study on Auto-Tuning Method of Learning Rate by Using Fuzzy Logic System,' Korea Fuzzy Logic and Intelligent Systems Society, pp. 484-489, 2003
5 Gail A. Carpenter and Stephen Grossberg, 'A Massively Parallel Architecture for a Self-organizing Neural Pattern Recognition Machine,' Computer Vision, Graphics, and Image Processing, vol. 37, pp. 54-115, 1987   DOI   ScienceOn
6 R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison Wedley, 1992
7 Gail A. Carpenter and Stephen Grossberg, 'The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Networks,' IEEE Computers, pp. 77-88, 1988
8 Rumelhart, D. E., Hintion, G. E., & Williams, R. J., 'Learning representation by back propagation errors.' Nature, 323, 533-536, 1986   DOI   ScienceOn
9 K. B. Kim and K. C. Kim, 'A Study on Face Recognition using New Fuzzy ART,' Proceedings of ITC-CSCC, vol. 2, pp. 1057-1060, 1998
10 J. U. Ryu, T. K. Kim, K. B. Kim, 'An Enhanced Fuzzy ART Algorithm for The Identifier Recognition form Shiping Container image,' Korea Fuzzy Logic and Intelligent Systems Society, vol. 12, no. 2, pp. 365-369, 2002
11 R. A. Jacobs, 'Increased rates of convergence through learning rate adaptation,' Neural Networks, vol. 1, no. 4, pp. 295-308, 1988
12 T. K. Kim, et, al., 'An Educational Matters Administration System on The web by Using Image Recognition,' Korea Intelligent Information Systems Society, pp. 203-209, 2002
13 M. Georipoulos, G. L. Heileman and J. Huang, 'Properties of Learning Related to Pattern Diversity in ARTl,' Neural Networks, vol. 1, pp. 751-757, 1991
14 J. W. Kim, et, al., 'Auto-Tuning Method of Learning Rate for Performance Improvement of Back propagation Algorithm,' The Institute of Electronics Engineers of Korea, vol. 39, no. 4, pp. 19-26, 2002