Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies

Abstract

In this talk, we propose a number of adaptive prototype learning (APL) algorithms. They employ the same algorithmic scheme to determine the number and location of prototypes, but differ in the use of samples or the weighted averages of samples as prototypes, and also in the assumption of distance measures. To understand these algorithms from a theoretical viewpoint, we address their convergence properties, as well as their consistency under certain conditions. We also present the soft version of APL, in which a non- zero training error is allowed so as to enhance the generalization power of the resultant classifier. Applying the proposed algorithms to some UCI data, we demonstrate that they outperform condensed nearest neighbor (CNN), k-nearest neighbor (k-NN) and support vector machine (SVM) approaches in most cases.