Learning with incrementality

This source preferred by Hamid Bouchachia

This data was imported from DBLP:

Authors: Bouchachia, A.

Editors: King, I., Wang, J., Chan, L. and Wang, D.L.

http://www.informatik.uni-trier.de/~ley/db/conf/iconip/iconip2006-1.html

Journal: ICONIP (1)

Volume: 4232

Pages: 137-146

Publisher: Springer

DOI: 10.1007/11893028_16

This data was imported from Scopus:

Authors: Bouchachia, A.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 4232 LNCS

Pages: 137-146

eISSN: 1611-3349

ISSN: 0302-9743

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.

The data on this page was last updated at 04:40 on November 19, 2017.