Learning with incrementality

Abstract

Learning with adaptivity is a key issue in many nowadays applications. The most important aspect of such an issue is incremental learning (IL). This latter seeks to equip learning algorithms with the ability to deal with data arriving over long periods of time. Once used during the learning process, old data is never used in subsequent learning stages. This paper suggests a new IL algorithm which generates categories. Each is associated with one class. To show the efficiency of the algorithm, several experiments are carried out. © Springer-Verlag Berlin Heidelberg 2006.