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Efficient supervised learning with reduced training exemplars

Conference Paper


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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.

Publication Date


  • 2008

Citation


  • G. Nguyen, A. Bouzerdoum & S. Phung, "Efficient supervised learning with reduced training exemplars," in IEEE International Joint Conference on Neural Networks, 2008, pp. 2980-2986.

Scopus Eid


  • 2-s2.0-56349119447

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1696&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/691

Has Global Citation Frequency


Start Page


  • 2980

End Page


  • 2986

Place Of Publication


  • Hong Kong

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.

Publication Date


  • 2008

Citation


  • G. Nguyen, A. Bouzerdoum & S. Phung, "Efficient supervised learning with reduced training exemplars," in IEEE International Joint Conference on Neural Networks, 2008, pp. 2980-2986.

Scopus Eid


  • 2-s2.0-56349119447

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1696&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/691

Has Global Citation Frequency


Start Page


  • 2980

End Page


  • 2986

Place Of Publication


  • Hong Kong