DocumentCode
1166500
Title
On the capability of accommodating new classes within probabilistic neural networks
Author
Hoya, Tetsuya
Author_Institution
Lab. for Adv. Brain Signal Process., RIKEN, Saitama, Japan
Volume
14
Issue
2
fYear
2003
fDate
3/1/2003 12:00:00 AM
Firstpage
450
Lastpage
453
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;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/TNN.2003.809417
Filename
1189644
Link To Document