Efficient supervised learning with reduced training exemplars

In this article, we propose a new supervised learning

approach for pattern classification applications involving

large or imbalanced data sets. In this approach, a clustering

technique is employed to reduce the original training set into

a smaller set of representative training exemplars, represented

by weighted cluster centers and their target outputs. Based on

the proposed learning approach, two training algorithms are

derived for feed-forward neural networks. These algorithms

are implemented and tested on two pattern classification applications

- skin detection and image classification. Experimental

results show that with the proposed learning approach, it is

possible to design networks in a fraction of time taken by

the standard learning approach, without compromising the

generalization ability and overall classification performance.