Experience with adaptive probabilistic neural networks and adaptive general regression neural networks

By adapting separate smoothing parameters for each dimension, the classification accuracy of the the probabilistic neural network (PNN), and the estimation accuracy of the general regression neural network (GRNN) can both be greatly improved. Accuracy comparisons are given for 28 databases. In addition, the dimensionality of the problem and the complexity of the network can usually be simultaneously reduced. The price to be paid for these benefits is increased training time.<
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