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Theoretical Interpretations And Applications Of Radial Basis Function Networks (2003)
| Citations: | 2 - 0 self |
BibTeX
@MISC{Blanzieri03theoreticalinterpretations,
author = {Enrica Blanzieri and Enrico Blanzieri},
title = {Theoretical Interpretations And Applications Of Radial Basis Function Networks},
year = {2003}
}
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Abstract
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains.
Keyphrases
radial basis function network theoretical interpretation artificial neural network regularization network instanced-based learner dynamic learning algorithm support vector machine medical application medical domain corresponding learning algorithm knowledge-based network wavelet network several way fuzzy controller kernel estimator brief survey
