Minimum-cost associative processor for piecewise-hyperspherical classification

Authors:
Brian A. Telfer;David P. Casasent
Affiliations:
Carnegie Mellon University, USA;Carnegie Mellon University, USA
Venue:
Neural Networks
Year:
1993

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Abstract

A new algorithm is presented for generating a neural associative processor with piecewise-hyperspherical decision boundaries for difficult multiclass classification. Two important characteristics of the algorithm are that it represents each class with a near-minimum number of hyperspheres and has proven convergence properties. The algorithm generates hyperspheres sequentially for each class, with the first hyperspheres classifying more training vectors of that class than the later hyperspheres. If a limited number of hyperspheres (neurons) are desired, one can thus select those that correctly classify the largest number of training vectors. Classification results are presented for a three-class 3-dimensional distortion-invariant case study (invariant to changes in position, scale, and in-plane and out-of-plane rotation). For the case study, the new method is shown to give better recall accuracy with fewer weights than other neural network and conventional pattern recognition methods tested.