Combining Multiple Classifiers in Probabilistic Neural Networks

We first summarize main features of a new probabilistic approach to neural networks recently developed in a series of papers in the framework of statistical pattern recognition. We consider a simplifying binary approximation of the output variables and, in order to prevent the arising information loss, we propose to combine multiple solutions. However, instead of combining different a posteriori probabilities, we make a parallel use of the binary output vectors to compute the standard Bayesian classifier.