A multi-scale radial basis function neural network
July 7th, 2009
| Tags: active research, neural networks, patent, Portfolio, RBF
Collaborators: Dr. Giorgos Mountrakis
MSRBF Evaluation
Funders: University of Maine supporting patent application
Motivation: One of the constraints of radial basis function (RBF) neural networks revolves around thei ability to combine local functions of variable spreads. Development of a multi-scale RBF would allow RBFs to exhibit their full potential as data regressors and classifiers.
Methodology: The underlying idea is to develop a training mechanism that considers both local and global errors when evaluating candidate node functions.
Findings: The initial experiments are very encouraging in terms of accuracy, consistency and repeatability.
Relevant papers: Patent 7,577,626 awarded by the USPTO – Full patent.
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