Performance characterization of K-winner machines

The paper reports on new findings about the properties of K-winner machines (KWMs). The resulting theoretical model is sharply characterized in terms of generalization performance, and exhibits interesting features from an application perspective as well. The major novel aspect lies in connecting analytically the KWM framework to established methods, proposed by Vapnik and Cherkassky, for assessing a classifier's generalization performance.