Learning prototype-based classifiers by margin maximization

Abstract

We study classifiers based on prototypes, which classify data points by the nearest neighbor rule, namely a data point is labeled the class which has its nearest prototype. The prototype-based classifiers are trained by the max-margin principle, which is used by support vector machines. For each training data point, we consider the difference of the distances between the nearest prototype of the class to which the point belongs and the nearest one of the other classes. And then, we define a margin by the minimum value of these differences for all the training data points. We verify the efficiency of the obtained classifier by numerical experiments.