Enhancements to probabilistic neural networks

Abstract

Probabilistic neural networks (PNNs) learn quickly from examples in one pass and asymptotically achieve the Bayes-optimal decision boundaries. The major disadvantage of a PNN stems from the fact that it requires one node or neuron for each training pattern. Various clustering techniques have been proposed to reduce this requirement to one node per cluster center. The correct choice of clustering technique will depend on the data distribution, data rate, and hardware implementation. Adaptation of kernel shape provides a tradeoff of increased accuracy for increased complexity and training time. The technique described also provides a basis for automatic feature selection and dimensionality reduction.<>