Neuron selection for RBF neural network classifier based on data structure preserving criterion

IEEE Transactions on Neural Networks

Volumn 16, Issue 6, 2005, Pages 1531-1540

  Mao, K Z ^a   Huang, Guang Bin ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Data structure preserving; Neuron selection; RBF neural networks

Indexed keywords

COMPUTATIONAL METHODS; DATA STRUCTURES; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATRIX ALGEBRA; PATTERN RECOGNITION;

DATA STRUCTURE PRESERVING; HIDDEN LAYER NEURONS; NEURAL NETWORK CLASSIFIER; NEURON SELECTION;

RADIAL BASIS FUNCTION NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; COMPUTER SIMULATION; FACTUAL DATABASE; INFORMATION RETRIEVAL; METHODOLOGY; THEORETICAL MODEL;

ALGORITHMS; CLUSTER ANALYSIS; COMPUTER SIMULATION; DATABASES, FACTUAL; INFORMATION STORAGE AND RETRIEVAL; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED;

EID: 28244449236     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2005.853575     Document Type: Article

References (21)

1. J. Moody and C. Darken, "Fast learning in network of locally-tuned processing units," Neural Comput., vol. 1, pp. 281-294, 1989.
2. Z. Uykan, C. Guzelis, M. E. Celebi, and H. N. Koivo, "Analysis of input-output clustering for determining centers of RBFN," IEEE Trans. Neural Networks, vol. 11, no. 4, pp. 851-858, 2000.
3. W. Pedrycz, "Conditional fuzzy clustering in the design of radial basis function neural networks," IEEE Trans. Neural Networks, vol. 9, no. 4, pp. 601-612, 1998.
4. T. Kohonen, Self-Organizing Maps. Berlin, Germany: Springer-Verlag, 1995.
5. T. Poggio and F. Girosi, "Networks for approximation and learning," Proc. IEEE, vol. 78, no. 9, pp. 1481-1497, 1990.
6. J. Gonzalez, I. Rojas, J. Ortega, H. Pomares, F. J. Fernandez, and A. F. Diaz, "Multiobjective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation," IEEE Trans. Neural Networks, vol. 14, no. 6, pp. 1478-1495, 2003.
7. N. B. Karayiannis and M. M. Randolph-Gips, "On the construction and training of reformulated radial basis function neural networks," IEEE Trans. Neural Networks, vol. 14, no. 4, pp. 835-846, 2003.
8. S. Chen, S. A. Billings, and W. Luo, "Orthogonal least squares methods and their applications m nonlinear system identification," Int. J. Contr., vol. 50, no. 5, pp. 1873-1896, 1989.
9. S. Chen, C. F. Cowan, and P. M. Grant, "Orthogonal least squares learning algorithms for redial basis function networks," IEEE Trans. Neural Networks, vol. 2, no. 2, pp. 302-609, 1991.
10. K. Z. Mao and S. A. Billings, "Algorithms for minimal model structure detection in nonlinear dynamic system identification," Int. J. Contr., vol. 68, no. 2, pp. 311-330, 1997.
11. L. Rutkoski, "Adaptive probabilistic neural networks for pattern classification in time-varying environment," IEEE Trans. Neural Networks, vol. 15, no. 4, pp. 811-827, 2004.
12. R. Schokopf, K. K. Sung, C. J. C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. N. Vapnik, "Comparing support vector machines with Gaussian kernels to radial basis function classifiers," IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2758-2765, 1997.
13. K. Z. Mao, "RBF neural network center selection based on Fisher ratio class separability measure," IEEE Trans. Neural Networks, vol. 13, no. 5, pp. 1211-1217, 2002.
14. K. Z. Mao, "Fast orthogonal forward selection algorithm for features subset selection," IEEE Trans. Neural Networks, vol. 13, no. 5, pp. 1218-1224, 2002.
15. K. Z. Mao, "Orthogonal forward selection and backward elimination algorithms for feature subset selection," IEEE Trans. Syst., Man. Cybern., pt. B, vol. 34, no. 1, pp. 629-634, 2004.
16. W. J. Krzanowski, Principles of Multivariate Analysis: A User's Perspective. New York: Oxford Science, 1988.
17. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
18. P. A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach. London, U.K.: Prentice-Hall, 1982.
19. I. T. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986.
20. C. L. Blake and C. J. Merz. (1998) UCI repository of machine learning databases. Dept. of Information and Computer Science, Univ. of California, Irvine, CA. [Online] http://www.ics.uci.edu/mlearn/ Machine-Learning.html
21. W. Duch. Datasets used for classification: Comparison of results. Nicolaus Copernicus Univ., Torun, Poland. [Online] http://www.phys.uni. torun.pl/kmk/projects/datasets.html