Title :
On the capability of accommodating new classes within probabilistic neural networks
Author_Institution :
Lab. for Adv. Brain Signal Process., RIKEN, Saitama, Japan
fDate :
3/1/2003 12:00:00 AM
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.
Keywords :
feedforward neural nets; learning (artificial intelligence); pattern classification; flexible configuration; incremental learning; multilayer neural networks; network configuration; pattern classification; probabilistic neural networks; Assembly; Biological neural networks; Guidelines; Mechanical factors; Neural networks; Neurons; Pattern classification; Radial basis function networks; Robustness; Signal processing;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2003.809417
