By Topic

A clustering approach to incremental learning for feedforward neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Engelbrecht, A.P. ; Dept. of Comput. Sci., Pretoria Univ., South Africa ; Brits, R.

The sensitivity analysis approach to incremental learning presented by Engelbrecht and Cloete (1999) is extended in this paper. That approach selects at each subset selection interval only one new informative pattern from the candidate training set, and adds the selected pattern to the current training subset. This approach is extended with an unsupervised clustering of the candidate training set. The most informative pattern is then selected from each of the clusters. Experimental results are given to show that the clustering approach to incremental learning performs substantially better than the original approach

Published in:

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on  (Volume:3 )

Date of Conference: