Some applications of clustering in the design of neural networks

In this paper, we discuss the role of clustering techniques in the design of neural networks. Specifically, we address the issue in relation to two network paradigms: one based on back-propagation and the other based on radial basis functions. In the former case, we demonstrate, emprically, that by employing clustering techniques, the training effort may be drastically brought down. In the latter case, we demonstrate that clustering techniques can be employed to build more robust classifiers. We also discuss the role of clustering in the design of hierarchical systems. Specifically, we discuss a hierarchical system based on radial basis functions