Implementing probabilistic neural networks

A modified PNN training algorithm is proposed. The standard PNN, though requiring a very short training time, when implemented in hardware exhibits the drawbacks of being costly in terms of classification time and of requiring an unlimited number of units. The proposed modification overcomes the latter drawback by introducing an elimination criterion to avoid the storage of unnecessary patterns. The distortion in the density estimation introduced by this criterion is compensated for by a cross-validation procedure to adapt the network parameters. The present paper deals with a specific real-world application, i.e. handwritten character classification. The proposed algorithm makes is possible to realise the PNN in hardware and, at the same time, compensates for some inadequacies arising from the theoretical basis of the PNN, which does not perform well with small training sets.