On the capability of accommodating new classes within probabilistic neural networks

Abstract

To date, probabilistic neural networks (PNNs) have been widely used in various pattern classification tasks due to their robustness. In this paper, it is shown that by exploiting the flexible network configuration property, the PNN classifiers also exhibit the capability in accommodating new classes. This is verified by extensive simulation studies on using four different domain data sets for pattern classification tasks.