  The condensed nearest neighbor (CNN) algorithm is a heuristic for reducing
the number of prototypical points stored by a nearest neighbor classifier,
while keeping the classification rule given by the reduced prototypical set
consistent with the full set. I present an upper bound on the number of
prototypical points accumulated by CNN. The bound originates in a bound on the
number of times the decision rule is updated during training in the multiclass
perceptron algorithm, and thus is independent of training set size.
