Ellipsoid ART and ARTMAP for incremental unsupervised and supervised Learning

摘要

We introduce Ellipsoid-ART (EA) and Ellipsoid-ARTMAP (EAM) as a generalization of Hyper-sphere ART and Hypersphere-ARTMAP respectively. Our novel architectures are based on ideas rooted in Fuzzy-ART (FA) and Fuzzy-ARTMAP (FAM). While FA/FAM summarize input data using hyper-rectangles, EA/EAM utilize hyper-ellipsoids for the same purpose. Due to their learning rules, EA and EAM share virtually all properties and characteristics of their FA/FAM counterparts. Preliminary experimentation implies that EA and EAM are to be viewed as good alternatives to FA and FAM for data clustering and classification tasks. Extensive pseudo-code is provided in the appendices for computationally efficient implementations of EA/EAM training and performance phases.