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An evolutionary programming-based probabilistic neural networks construction technique

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2 Author(s)
Sarkar, M. ; Dept. of Comput. Sci. & Eng., Indian Inst. of Technol., Madras, India ; Yegnanarayana, B.

Quick learning ability of a probabilistic neural network (PNN) has made it a popular alternative to feedforward neural networks trained by backpropagation algorithm. However in real life applications, where training set size is quite large, the PNN model suffers from a huge memory overhead and a long testing time. Moreover, its generalization capability critically depends on the choice of certain architectural parameters, which are presently chosen in an ad hoc basis. Since the PNN can be viewed as a Gaussian mixture model, the above drawbacks can be avoided if the PNN is configured based on a Gaussian mixture model with an optimum parameter set. In this paper, an evolutionary programming based clustering technique is employed to determine the optimum parameter set of the Gaussian mixture model. The efficacy of the proposed scheme is demonstrated on a Contract Bridge game opening bid problem

Published in:

Neural Networks,1997., International Conference on  (Volume:1 )

Date of Conference:

9-12 Jun 1997