A soft probabilistic neural network for implementation of Bayesian classifiers
Mohammad Bagher Menhaj, Farshid Delgosha · 2002
A classifier with the optimum decision, Bayesian classifier could be implemented with probabilistic neural networks (PNNs). The authors presented a new competitive learning algorithm for training such a network when all classes are completely separated. This paper generalizes our previous work to the case of overlapping categories. In our new perspective, the network is, in fact, made blind with respect to the overlapping training samples, so the new training algorithm is called soft PNN (or SPNN). The usefulness of SPNN has been proved by two 2-D classification problems. The simulation results highlight the merit of the proposed method.