Neural classifiers and statistical pattern recognition: Applications for currently established links

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 27, Issue 3, 1997, Pages 488-497

  Osman, Hossam ^b   Fahmy, Moustafa M ^a,c  

^a IEEE

^b QUEEN S UNIVERSITY   (Canada)

^c KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS   (Saudi Arabia)

Author keywords

Artificial neural networks; Bayes decision theory; Discriminant analysis; Mean square error optimization; Pattern classification

Indexed keywords

BACKPROPAGATION; DECISION THEORY; ELECTRIC NETWORK TOPOLOGY; ERRORS; ESTIMATION; LEARNING ALGORITHMS; NEURAL NETWORKS; OPTIMIZATION; PERFORMANCE;

BAYES DECISION THEORY; MEAN SQUARE ERROR OPTIMIZATION; PATTERN CLASSIFICATION; STANDARD MEAN SQUARE ERROR; STATISTICAL PATTERN RECOGNITION;

PATTERN RECOGNITION;

EID: 0031162785     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/3477.584955     Document Type: Article

References (23)

1. D. W. Ruck, S. K. Rogers, M. Kabrisky, M. E. Oxley, and B. W. Suter, "The multilayer perceptron as an approximation to a Bayes optimal discriminant function," IEEE Trans. Neural Networks, vol. 1, pp. 296-298, 1990.
2. J. B. Hampshire and B. Pearlmutter, "Equivalence proofs for multi-layer perceptron classifiers and the Bayesian discriminant function," in Proc. 1990 Connectionist Models Summer School.
3. E. A. Wan, "Neural network classification: A Bayesian interpretation," IEEE Trans. Neural Networks, vol. 1, pp. 303-305, 1990.
4. F. Kanaya and S. Miyake, "Bayes statistical behavior and valid generalization of pattern classifying neural networks," IEEE Trans. Neural Networks, vol. 2, pp. 471-475, 1991.
5. M. D. Richard and R. P. Lippmann, "Neural network classifiers estimate Bayesian a posteriori probabilities," Neural Computation, vol. 3, pp. 461-483, 1991.
6. A. R. Webb and D. Lowe, "The optimized internal representation of multilayer classifier networks performs nonlinear discriminant analysis," Neural Networks, vol. 3, pp. 367-375, 1990.
7. D. Lowe and A. R. Webb, "Optimized feature extraction and the Bayes decision in feed-forward classifier networks," IEEE Trans. Pattern Anal. Machine Intell., vol. 13, no. 4, pp. 355-364, 1991.
8. P. Gallinari, S. Thiria, F. Badran, and F. Fogelman-Soulie, "On the relations between discriminant analysis and mutlilayer perceptrons," Neural Networks, vol. 4, pp. 349-360, 1991.
9. H. Osman and M. M. Fahmy, "Back-propagation and radial-basis-function networks for pattern discrimination," in Proc. World Congr. Neural Networks (WCNN), Portland, OR, 1993, vol. IV, pp. 181-185.
10. _, "On the discriminatory power of adaptive feed-forward layered networks," IEEE Trans. Pattern Anal. Machine Intell., vol. 16, no. 8, pp. 837-842, 1994.
11. D. Lowe and A.R. Webb, "Exploiting prior knowledge in network optimization: An illustration from medical prognosis," Network, vol. 1, pp. 299-323, 1990.
12. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
13. W. G. Wee, "Generalized inverse approach to adaptive multiclass pattern classification," IEEE Trans. Comput., vol. 17, pp. 1157-1164, 1968.
14. K. Fukunaga, Introduction to Statistical Pattern Recognition. New York: Academic, 1990.
15. K. Fukunaga and R.D. Short, "A class of feature extraction criteria and its relation to the bayes risk estimate," IEEE Trans. Inform. Theory, vol. 26, pp. 59-65, 1980.
16. R. P. Gorman and T. J. Sejnowski, "Learned classification of sonar targets using a massively parallel network," IEEE Trans. Acoust., Speech, Signal Processing, vol. 36, pp. 1135-1140, 1988.
17. H. Osman and M. M. Fahmy, "A new measure of the generalization of neural network classifiers," in Proc. World Congr. Neural Networks (WCNN), Portland, OR, 1993, vol. IV, pp. 186-189.
18. K. Fukunaga and D. H. Hummels, "Bias of nearest neighbor estimates," IEEE Trans. Pattern Anal. Machine Intell., vol. 9, pp. 103-112, 1987.
19. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes in C: The Art of Scientific Computing. New York: Cambridge Univ. Press, 1989.
20. D. Lowe, "Adaptive radial basis function nonlinearities, and the problem of generalization," in Proc. 1st IEE Int. Conf. Artificial Neural Networks, London, U.K., 1989, pp. 171-175.
21. J. Moody and C. J. Darken, "Fast learning in networks of locally-tuned processing units," Neural Computation, vol. 1, pp. 281-294, 1989.
22. S. Nowlan, "Maximum likelihood competitive learning," in Advances in Neural Information Processing Systems 2, D.S. Touretzky, Ed. San Mateo, CA: Morgan Kaufmann, 1990, pp. 574-582.
23. H. Osman and M. M. Fahmy, "Probabilistic winner-take-all learning algorithms for radial-basis-function neural classifiers," Neural Computation, vol. 6, pp. 925-941, 1994.