The Random Neural Network (RNN) has received, since its inception in 1989, considerable
attention and has been successfully used in a number of applications. In this critical review …

Image classifiers are largely adopted to categorize a pool of images or patterns in a
databank, match category of a query image and to retrieve similar images to query from the …

It is crucial for cancer diagnosis and treatment to accurately identify the site of origin of a
tumor. With the emergence and rapid advancement of DNA microarray technologies …

In this paper, we present the evolution of adaptive resonance theory (ART) neural network
architectures (classifiers) using a multiobjective optimization approach. In particular, we …

This paper focuses on classification problems, and in particular on the evolution of ARTMAP
architectures using genetic algorithms, with the objective of improving generalization …

The focus of this paper is semi-supervised learning in the context of pattern recognition.
Semi-supervised learning (SSL) refers to the semi-supervised construction of clusters during …

In this paper we introduce boosted ellipsoid ARTMAP (bEAM), a variant of ellipsoid
ARTMAP, which via a tunable misclassification error tolerance increases the network's …

This paper focuses on the evolution of Fuzzy ARTMAP neural network classifiers, using
genetic algorithms, with the objective of improving generalization performance (classification …

Correct classification is crucial to cancer diagnosis and treatment. We demonstrate that a
new family of neural network architectures-Ellipsoid ART and ARTMAP (EA/EAM)-can …

Fuzzy ARTMAP neural networks have been proven to be good classifiers on a variety of
classification problems. However, the time that Fuzzy ARTMAP takes to converge to a …