Generalization of the Eisler-Ekman Similarity Function

Abstract

A generalization of the Eisler-Ekman similarity function is presented which fits data at least as well as some of the other similarity functions that have been proposed. While these functions either are parameterless or merely contain correcting parameters added to improve goodness of fit, the general similarity function has two parameters that are inherent to the model of similarity which the function represents.