On a Simple Minkowski Metric Classifier

A classifier which, in general, implements a nonlinear decision boundary is shown to be equivalent to a linear discriminant function when the measurements are binary valued; its relation to the Bayes classifier is derived. The classifier requires less computation than a similar one based on the Euclidean distance and can perform equally well.