Pioneering work in the 1940s and 1950s suggested that the concept of 'chunking'might be
important in many processes of perception, learning and cognition in humans and animals …

A neural network recognition and tracking system is proposed for classification of radar
pulses in autonomous Electronic Support Measure systems. Radar type information is …

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

Representing cognitive processes remains one of the great research challenges. Many
important application areas, such as clinical diagnosis, operate in an environment of relative …

As an extension of on-line learning, life-long learning challenges a system which is exposed
to patterns from a changing environment during its entire lifespan. An autonomous system …

We address the problem of Topic Detection and Tracking (TDT) and subsequently detecting
trends from a stream of text documents. Formulating TDT as a clustering problem in a class …

Neuro-fuzzy systems have been in the focus of recent research as a solution to jointly exploit
the main features of fuzzy logic systems and neural networks. Within the application …

Real-time neural-network models provide a conceptual framework for formulating questions
about the nature of cognition, an architectural framework for mapping cognitive functions to …

Neural network classifiers have been shown to provide supervised classification results that
significantly improve on traditional classification algorithms such as the Bayesian (maximum …

In this paper we are examining the issue of overtraining in Fuzzy ARTMAP. Over-training in
Fuzzy ARTMAP manifests itself in two different ways:(a) it degrades the generalization …