Abstract

Incremental learning (IL) plays a key role in many real-world applications where data arrives over time. It is mainly concerned with learning models in an ever-changing environment. In this paper, we review some of the incremental learning algorithms and evaluate them within the same experimental settings in order to provide as objective comparative study as possible. These algorithms include fuzzy ARTMAP, nearest generalized exemplar, growing neural gas, generalized fuzzy min-max neural network, and IL based on function decomposition (ILFD).