Collaborators: Dr. Giorgos Mountrakis
Funders: University of Maine covered patent application fees.
Motivation: One of the constraints of radial basis function (RBF) neural networks revolves around the ability to combine local functions of variable spreads. Development of a multi-scale RBF would allow RBFs to exhibit their full potential in regression and classification tasks from multiple disciplines.
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. A manuscript publication is under development.