Neural Network Classification: A Bayesian Interpretation

IEEE Transactions on Neural Networks

Volumn 1, Issue 4, 1990, Pages 303-305

  Wan, Eric A ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL TECHNIQUES - LEAST SQUARES APPROXIMATIONS; STATISTICAL METHODS - APPLICATIONS;

CLASSIFICATION PROBLEM; NEURAL NETWORK TRAINING;

NEURAL NETWORKS;

EID: 0025597157     PISSN: 10459227     EISSN: 19410093     Source Type: Journal    
DOI: 10.1109/72.80269     Document Type: Article

References (6)

1. A. Papoulis, Probability, Random Variables, and Stochastic Processes. New York: McGraw-Hill, 1965.
2. D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. Cambridge, MA: M.I.T. Press, 1986.
3. H. White, “Learning in artificial neural networks: A statistical perspective,” Neural Comput., vol. 1, no. 4, pp. 425-464, Winter 1989.
4. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
5. N. Tishby, E. Levin, and S. A. Solla, “Consistent inference of probabilities in layered networks: Predictions and generalization,” in Proc. Int. Joint Conf. Neural Networks, Washington, DC, June 1989, vol. 2, pp. 403-410.
6. L. G. Vapnik, Estimation and Dependences Based on Empirical Data. New York: Springer-Verlag, 1982, pp. 162-180.