Abstract
The nearest-neighbor rule and the potential-function classifier are nonparametric discrimination methods that require the storage of a set of sample patterns. Here, a relationship between the two methods in terms of subclasses and superclasses is developed. Considering an exponential potential function, necessary and sufficient conditions for identity of their decision surfaces are obtained. Based on these conditions, an algorithm for establishing identity is introduced.
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