Learning vector quantization for the probabilistic neural network

Authors:
P. Burrascano
Affiliations:
INFO-COM Dept., Roma Univ.
Venue:
IEEE Transactions on Neural Networks
Year:
1991

Citing 0
Cited 16

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Abstract

A modified version of the PNN (probabilistic neural network) learning phase which allows a considerable simplification of network structure by including a vector quantization of learning data is proposed. It can be useful if large training sets are available. The procedure has been successfully tested in two synthetic data experiments. The proposed network has been shown to improve the classification performance of the LVQ (learning vector quantization) procedure