Median Radial Basis Functions Neural Network (1996)
| Venue: | IEEE Trans. on Neural Networks |
| Citations: | 23 - 14 self |
BibTeX
@ARTICLE{Bors96medianradial,
author = {Adrian G. Bors and Ioannis Pitas},
title = {Median Radial Basis Functions Neural Network},
journal = {IEEE Trans. on Neural Networks},
year = {1996},
volume = {7},
pages = {1351--1364}
}
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OpenURL
Abstract
Radial Basis Functions (RBF) consists of a two-layer neural network, where each hidden unit implements a kernel function. Each kernel is associated with an activation region from the input space and its output is fed to an output unit. In order to find the parameters of a neural network which embeds this structure we take into consideration two different statistical approaches. The first approach uses classical estimation in the learning stage and it is based on the learning vector quantization algorithm and its second order statistics extension. After the presentation of this approach, we introduce the Median Radial Basis Functions (MRBF) algorithm based on robust estimation of the hidden unit parameters. The proposed algorithm employs the marginal median for kernel location estimation and the median of the absolute deviations for the scale parameter estimation. A histogram-based fast implementation is provided for the MRBF algorithm. The theoretical performance of the two training al...







