This survey samples from the ever-growing family of adaptive resonance theory (ART)
neural network models used to perform the three primary machine learning modalities …

Adaptive Resonance Theory (ART) is considered as an effective approach for realizing
continual learning thanks to its ability to handle the plasticity-stability dilemma. In general …

This article presents an adaptive resonance theory predictive mapping (ARTMAP) model,
which uses incremental cluster validity indices (iCVIs) to perform unsupervised learning …

This article proposes a multi-label classification algorithm capable of continual learning by
applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the …

In streaming data applications, the incoming samples are processed and discarded, and
therefore, intelligent decision-making is crucial for the performance of lifelong learning …

Most algorithms for steering, obstacle avoidance, and moving object detection rely on
accurate self-motion estimation, a problem animals solve in real time as they navigate …

Many network-based tasks need powerful feature expression to capture the diversity of
networks. It can be provided by network embedding learning of nodes. The related …

Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes
and phenotypes (signs and symptoms of disease), genetic mutations to altered protein …

In general, a similarity threshold (ie, a vigilance parameter) for a node learning process in
Adaptive Resonance Theory (ART)-based algorithms has a significant impact on clustering …

Several methods exist to detect and distinguish collisions of robotic systems with their
environment, since this information is a critical dependency of many tasks. These methods …