Pattern classifiers with adaptive distances

This paper presents learning vector quantization classifiers with adaptive distances. The classifiers furnish discriminant class regions from the input data set that are represented by prototypes. In order to compare prototypes and patterns, the classifiers use adaptive distances that change at each iteration and are different from one class to another or from one prototype to another. Experiments with real and synthetic data sets demonstrate the usefulness of these classifiers.