Efficient Supervised Learning with Reduced Training Exemplars

Authors

Giang H. Nguyen, University of WollongongFollow
A. Bouzerdoum, University of WollongongFollow
Son Lam Phung, University of WollongongFollow

RIS ID

24100

Publication Details

This conference paper was originally published as Nguyen, GH, Bouzerdoum, A, Phung, SL, Efficient Supervised Learning with Reduced Training Exemplars, 2008 International Joint Conference on Neural Networks (IJCNN 2008), Hong Kong, 1-6 June 2008, 2981-2987. Copyright Institute of Electrical and Electronics Engineers 2008. Original conference paper available here

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.