Probabilistic neural network training procedure with the use of SARSA algorithm

Abstract

In this paper, we present new probabilistic neural network (PNN) training procedure for classification problems. Proposed procedure utilizes the State-Action-Reward-State-Action algorithm (SARSA in short), which is the implementation of the reinforcement learning method. This algorithm is applied to the adaptive selection and computation of the smoothing parameter of the PNN model. PNNs with different forms of the smoothing parameter are regarded. The prediction ability for all the models is assessed by computing the test error with the use of a 10-fold cross validation (CV) procedure. The obtained results are compared with state-of-the-art methods for PNN training.