Approximation of Bayesian Discriminant Function by Neural Networks in Terms of Kullback-Leibler Information

  • Authors:

  • Yoshifusa Ito;Cidambi Srinivasan

  • Affiliations:

  • -;-

  • Venue:
  • ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
  • Year:
  • 2001

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Abstract

Following general arguments on approximation Bayesian discriminant functions by neural networks, rigorously proved is that a three layered neural network, having rather a small number of hidden layer units, can approximate the Bayesian discriminant function for the two category classification if the log ratio of the a posteriori probability is a polynomial. The accuracy of approximation is measured by the Kullback-Leibler information. An extension to the multi-category case is also discussed.