Incremental learning by decomposition

This source preferred by Hamid Bouchachia

This data was imported from DBLP:

Authors: Bouchachia, A.

Editors: Wani, M.A., Li, T., Kurgan, L.A., Ye, J. and Liu, Y.

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4041453

Journal: ICMLA

Pages: 63-68

Publisher: IEEE Computer Society

DOI: 10.1109/ICMLA.2006.28

This data was imported from Scopus:

Authors: Bouchachia, A.

Journal: Proceedings - 5th International Conference on Machine Learning and Applications, ICMLA 2006

Pages: 63-68

ISBN: 9780769527352

DOI: 10.1109/ICMLA.2006.28

Adaptivity in neural networks aims at equipping learning algorithms with the ability to self-update as new training data becomes available. In many application, data arrives over long periods of time, hence the traditional one-shot training phase cannot be applied. The most appropriate training methodology in such circumstances is incremental learning (IL). The present paper introduces a new IL algorithm dedicated to classification problems. The basic idea is to incrementally generate prototyped categories which are then linked to their corresponding classes. Numerical simulations show the performance of the proposed algorithm. © 2006 IEEE.

This data was imported from Web of Science (Lite):

Authors: Bouchachia, A.

Journal: ICMLA 2006: 5th International Conference on Machine Learning and Applications, Proceedings

Pages: 63-68

ISBN: 978-0-7695-2735-2

The data on this page was last updated at 04:54 on April 18, 2019.