Performance evaluation of recurrent RBF network in nearest neighbor classification

Authors:
Mehmet Kerem Müezzinoğlu
Affiliations:
Computational Intelligence Lab., University of Louisville, Louisville, KY
Venue:
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
Year:
2005

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Abstract

Superposition of radial basis functions centered at given prototype patterns constitutes one of the most suitable energy forms for gradient systems that perform nearest neighbor classification with real-valued static prototypes. It has been shown in [1] that a continuous-time dynamical neural network model, employing a radial basis function and a sigmoid multi-layer perceptron sub-networks, is capable of maximizing such an energy form locally, thus performing almost perfectly nearest neighbor classification, when initiated by a distorted pattern. This paper reviews the proposed design procedure and presents the results of the intensive experimentation of the classifier on random prototypes.