Abstract
The usability of the constructive neural algorithms as pattern classifiers is investigated. It is pointed out that the unboundedness of the decision regions formed by most neural recognizers leads to substantial limitations of the generalization capabilities of these nets. We specify a constructive neural recognizer that forms bounded decision regions, and report the results of this algorithm on a series of benchmark problems that resemble the usual pattern recognition problems.
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