Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification

Abstract

This paper introduces a learning algorithm that can be used for training reformulated radial basis function neural networks (RBFNNs) capable of identifying uncertainty in data classification. This learning algorithm trains a special class of reformulated RBFNNs, known as cosine RBFNNs, by updating selected adjustable parameters to minimize the class… (More)
DOI: 10.1109/TNN.2006.877538

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Cite this paper

@article{Karayiannis2006TrainingRR, title={Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification}, author={Nicolaos B. Karayiannis and Yaohua Xiong}, journal={IEEE Transactions on Neural Networks}, year={2006}, volume={17}, pages={1222-1234} }