A clustering approach to incremental learning for feedforward neural networks

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.