Abstract
-
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.