This paper presents a novel adaptive resonance theory (ART)-based modular architecture
for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA) …

Clusters retrieved by generic Adaptive Resonance Theory (ART) networks are limited to
their internal categorical representation. This study extends the capabilities of ART by …

The clustering structures formed by Adaptive Resonance Theory (ART) and many other
algorithms are dependent on input presentation/permutation order. In this work, we exploit …

Despite the advantages of fast and stable learning, Adaptive Resonance Theory (ART) still
relies on an empirically fixed vigilance parameter value to determine the vigilance regions of …

This paper proposes a novel neural network architecture based on adaptive resonance
theory (ART) called ARTgrid that can perform both online and offline clustering of 2D object …

Conventional unsupervised learning algorithms require knowledge of the desired number of
clusters beforehand. Even if such knowledge is not required in advance, empirical selection …

One of the distinguishing features of Adaptive Resonance Theory (ART) is that it relies on a
second similarity check, called a vigilance test, to accept or reject a sample into a given …

This paper introduces the Adaptive Resonance Theory under Constraint (ART-C 2A)
learning paradigm based on ART 2A, which is capable of generating a user-defined number …

Hierarchical clustering is an important and powerful but computationally extensive
operation. Its complexity motivates the exploration of highly parallel approaches such as …

Abstract Fuzzy ART and Fuzzy ARTMAP are two prominent neural network architectures
based on the principles of Grossberg's Adaptive Resonance Theory (ART). While the former …