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The Rapid Kernel Classifier: A Link between the Self-Organizing Feature Map and the Radial Basis Function Network

Department of Electrical and Computer Engineering The University of Texas Austin, TX 78712

Srinivasa V. Chakravarthy

Department of Electrical and Computer Engineering The University of Texas Austin, TX 78712

The learning dynamics of a Radial Basis Function (RBF) network is shown to be related to the Learning Vector Quantization algorithm. Based on this similarity, a hybrid training scheme for the RBF network is proposed. The resulting Rapid Kernel Classifier is evaluated using a 6-class radar data set. Considerable speedup in training is obtained with this new scheme. Also, for the one-dimensional case, we prove that the distribution of centroids of the RBF network ap proaches node density of the Self-Organizing Feature Map as a limit. This result suggests a deeper connection between the fundamental learning paradigms, namely supervised and unsupervised learning.

Journal of Intelligent Material Systems and Structures, Vol. 5, No. 2, 211-219 (1994)
DOI: 10.1177/1045389X9400500207


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