Probabilistic Neural Network - parameters adjustment in classification task

This work presents a comparative analysis of probabilistic neural network training methods applied to achieve best performance in various classification tasks. Two result from classical mathematical methods based on the theory of kernel density estimators: the plug-in method and cross-validation procedure. The other two methods are more advanced: a metaheuristic algorithm of particle swarm optimization, and a procedure based on reinforcement learning. Ten data sets, regarded in eleven classification problems, taken from the UCI repository are used for the numerical analysis. A comparative analysis of probabilistic neural network learning methods leads to interesting conclusions. Although it does not allow for unambiguous selection of the best learning method, it provides a possibility of choosing a method that is adequate for the given conditions. The description of this is included in the work.