@article{c154bb5d0b0742378f6ba740394f18a5,
title = "An algorithm to generate radial basis function (RBF)-like nets for classification problems",
abstract = "This paper presents a new algorithm for generating radial basis function (RBF)-like nets for classification problems. The method uses linear programming (LP) models to train the RBF-like net. Polynomial time complexity of the method is proven and computational results are provided for many well-known problems. The method can also be implemented as an on-line adaptive algorithm.",
keywords = "Classification problems, Linear programming models, Radial basis function-like nets",
author = "Asim Roy and Sandeep Govil and Raymond Miranda",
note = "Funding Information: 1. INTRODUCTION--A ROBUST AND EFFICIENT LEARNING THEORY The science of artificial neural networks needs a robust theory for generating neural networks and for adaptation. Lack of a robust learning theory has been a significant impediment to the successful application of neural networks. A good, rigorous theory for artificial neural networks should include learning methods that adhere to the following stringent performance criteria and tasks. 1. Perform network design task. A neural network learning method must be able to design an appro-priate network for a given problem, because it is a task performed by the brain. A predesigned net should not be provided to the method as part of its external input, because it never is an external input to the brain. 2. Robustness in learning: The method must be robust so as not to have the local minima problem, the problems of oscillation, and catastrophic forgetting or similar learning difficulties. The brain does not exhibit such problems. 3. Quickness in learning: The method must be quick in its learning and learn rapidly from only a few examples, much as humans do. For example, a method that learns from only 10 examples (on-line) learns faster than one that needs 100 or 1000 ex- Acknowledgement: This research was supported, in part, by the National ScienceF oundationg rant IRI-9113370 and Collegeo f Business Summer Grants. Copyright: Copyright 2014 Elsevier B.V., All rights reserved.",
year = "1995",
doi = "10.1016/0893-6080(94)00064-S",
language = "English (US)",
volume = "8",
pages = "179--201",
journal = "Neural Networks",
issn = "0893-6080",
publisher = "Elsevier Limited",
number = "2",
}