The ability of artificial agents to increment their capabilities when confronted with new data is
an open challenge in artificial intelligence. The main challenge faced in such cases is …

In contrast to batch learning where all training data is available at once, continual learning
represents a family of methods that accumulate knowledge and learn continuously with data …

The ability to incrementally learn new classes is crucial to the development of real-world
artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot …

A well-known issue for class-incremental learning is the catastrophic forgetting
phenomenon, where the network's recognition performance on old classes degrades …

An incremental network model is introduced which is able to learn the important topological
relations in a given set of input vectors by means of a simple Hebb-like learning rule. In …

We present a new self-organizing neural network model that has two variants. The first
variant performs unsupervised learning and can be used for data visualization, clustering …

The ability to grow extra nodes is a potentially useful facility for a self-organising neural
network. A network that can add nodes into its map space can approximate the input space …

Clustering is a fundamental data analysis method. It is widely used for pattern recognition,
feature extraction, vector quantization (VQ), image segmentation, function approximation …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …

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 …