An Information Criterion for Optimal Neural Network Selection

IEEE Transactions on Neural Networks

Volumn 2, Issue 5, 1991, Pages 490-497

  Fogel, David B ^a  

^a LOCKHEED MARTIN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

STATISTICAL METHODS;

AKAIKE'S INFORMATION CRITERION; BINARY CLASSIFICATION PROBLEMS; INFORMATION STATISTIC; STATISTICAL MODEL IDENTIFICATION;

NEURAL NETWORKS;

EID: 0026221027     PISSN: 10459227     EISSN: 19410093     Source Type: Journal    
DOI: 10.1109/72.134286     Document Type: Article

References (25)

1. M. Gutierrez, J. Wang, and R. Grondin, “Estimating hidden unit number for two-layer perceptrons,” in Proc. Int. Joint Conf. Neural Networks (Washington, DC), 1989, pp. 677–681.
2. S. Y. Kung and J. N. Hwang, “An algebraic projection analysis for optimal hidden units size and learning rate in back-propagation learning,” in Proc. IEEE Int. Conf. Neural Networks (San Diego, CA), 1988, pp. 363–370.
3. D. C. Montgomery and E. A. Peck, Introduction to Linear Regression Analysis. New York: Wiley, 1982.
4. C. L. Mallows, “Choosing variables in a linear regression: A graphical aid,” presented at the Central Regional Meeting of the Institute of Mathematical Statistics, Manhattan, KS, 1964.
5. C. L. Mallows, “Choosing a subset regression,” presented at the Joint Statistical Meetings, Los Angeles, CA, 1966.
6. C. L. Mallows, “Some comments on Cp,” Technometrics, vol. 15, pp. 661–675, 1973.
7. A. R. Barron, “Predicted squared error: A criterion of automatic model selection,” in Self-Organizing Methods in Modeling, S. J. Farlow, Ed. New York: Marcel Dekker, 1984.
8. H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Automat. Contr., vol. AC 19, no. 6, pp.716–723, 1974.
9. S. Kullback, Information Theory and Statistics. New York: Wiley, 1959.
10. H. Cramer, Mathematical Methods of Statistics. Princeton, NJ: Princeton University Press, 1946.
11. P. J. Huber, “The behavior of maximum likelihood estimate under nonstandard conditions,” in Proc. 5th Berkeley Symp. Math. Statistics and Prob., 1967, pp. 221–223.
12. H. Cramer, Random Variables and Probability Distributions (Cambridge Tracts in Mathematics, no. 36) Cambridge: Cambridge University Press, 1937.
13. H. Cramer, The Elements of Probability Theory and Some of its Applications. New York: Wiley, 1955.
14. D. A. S. Fraser, Nonparametric Methods in Statistics. New York: Wiley, 1957.
15. S. Kullback, Information Theory and Statistics. New York: Dover, 1968 (reprint).
16. H. Akaike, “A Bayesian extension of the minimum AIC procedure of autoregressive model fitting,” Biometrika, vol. 66, no. 2, pp. 237–242, 1979.
17. R. Shibata, “Selection of the order of an autoregressive model by Akaike’s information criterion,” Biometrika, vol. 63, pp. 117–126, 1976.
18. Q. Zhang, K. M. Wong, P. C. Yip, and J. P. Reilly, “Statistical analysis of the performance of information theoretic criteria in the detection of the number of signals in array processing,” IEEE Trans. Acoust., Speech, Signal Process., vol. 37, pp. 1557–1567, 1989.
19. D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing: Exploration in the Microstructure of Cognition, vol. 1. Cambridge, MA: MIT Press, 1986.
20. D. B. Fogel, L. J. Fogel, and V. W. Porto, “Evolving neural networks,” Biol. Cybern., vol. 63, pp. 487–493, 1990.
21. A. Papoulis, Probability, Random Variables, and Stochastic Processes, 2nd ed. New York: McGraw-Hill, 1984.
22. D. B. Fogel, “An evolutionary approach to the traveling salesman problem,” Biol. Cybern., vol. 60, pp. 134–144, 1988.
23. G. Schwarz, “Estimating the dimension of a model,” Ann. Statist., vol. 6, no. 2, pp. 461–464, 1977.
24. J. Rissanen, “Modeling by shortest data description,” Automatica, vol. 14, pp. 465–471, 1978.
25. A. R. Barron, “Complexity approach to estimating the order of a model,” Electrical Engineering 378B Final Report, Information Systems Laboratory, Stanford University, 1982.