Stateless Q-Learning Algorithm for Training of Radial Basis Function Based Neural Networks in Medical Data Classification

Abstract

In this article, the stateless Q-learning algorithm is used for the training process of two radial basis function based models: the radial basis function neural network (RBFNN) and the probabilistic neural network (PNN). The training process of considered models consists in the initialization and the adaptation of the smoothing parameter of the networks’ activation function in a hidden layer. The main idea of this approach is based on the appropriate computation of the smoothing parameter which relies on its update according to the stateless Q-learning algorithm. The proposed method is tested on six commonly available repository data sets. The prediction ability of the algorithm is assessed by computing the test set error on 10%, 20%, 30%, and 40% of examples drawn randomly from the entire input data. Obtained results are compared with the test errors achieved by PNN trained by means of the conjugate gradient procedure. It is shown that Q-learning method can be applied to the automatic adaptation of the smoothing parameter for both neural networks and provides better prediction ability results.