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 …

Learning in neural networks can broadly be divided into two categories, viz., off-line (or
batch) learning and online (or incremental) learning. In this paper, a review of a variety of …

We introduce Learn++, an algorithm for incremental training of neural network (NN) pattern
classifiers. The proposed algorithm enables supervised NN paradigms, such as the …

This second edition of the must-read work in the field presents generic computational
models and techniques that can be used for the development of evolving, adaptive modeling …

This paper presents an on-line unsupervised learning mechanism for unlabeled data that
are polluted by noise. Using a similarity threshold-based and a local error-based insertion …

Recent years have witnessed an incredibly increasing interest in the topic of incremental
learning. Unlike conventional machine learning situations, data flow targeted by incremental …

We have previously introduced an incremental learning algorithm Learn++, which learns
novel information from consecutive data sets by generating an ensemble of classifiers with …

Many methods and models have been proposed for solving difficult problems such as
prediction, planning and knowledge discovery in application areas such as bioinformatics …

Artificial neural networks (ANNs) are comparatively straightforward to understand and use in
the analysis of scientific data. However, this relative transparency may encourage their use …

In this work it is shown how fuzzy lattice neurocomputing (FLN) emerges as a connectionist
paradigm in the framework of fuzzy lattices (FL-framework) whose advantages include the …