Fuzzy kernel associative memories with application in classification

Abstract

In this paper we introduce the class of fuzzy kernel associative memories (fuzzy KAMs). Fuzzy KAMs are derived from single-step generalized exponential bidirectional fuzzy associative memories by interpreting the exponential of a fuzzy similarity measure as a kernel function. The output of a fuzzy KAM is obtained by summing the desired responses weighted by a normalized evaluation of the kernel function. Furthermore, in this paper we propose to estimate the parameter of a fuzzy KAM by maximizing the entropy of the model. We also present two approaches for pattern classification using fuzzy KAMs. Computational experiments reveal that fuzzy KAM-based classifiers are competitive with well-known classifiers from the literature.