Skip to main content
placeholder image

Efficient SVM training with reduced weighted samples

Conference Paper


Download full-text (Open Access)

Abstract


  • This paper presents an efficient training approach for support vector machines that will improve their ability to learn from a large or imbalanced data set. Given an original training set, the proposed approach applies unsupervised learning to extract a smaller set of salient training exemplars,

    which are represented by weighted cluster centers and the target outputs. In subsequent supervised learning, the objective function is modified by introducing a weight for each new training sample and the corresponding penalty term. In this paper, we investigate two methods of defining the weight based

    on cluster vectors. The proposed SVM training is implemented and tested on two problems: (i) gender classification of facial images using the FERET data set; (ii) income prediction using the UCI Adult Census data set. Experiment results show that compared to standard SVM training, the proposed approach leads to much faster SVM training, produces a more compact classifier while maintaining generalization ability.

Publication Date


  • 2010

Citation


  • Nguyen, G. Hoang., Phung, S. & Bouzerdoum, A. (2010). Efficient SVM training with reduced weighted samples. IEEE World Congress on Computational Intelligence (pp. 1764-1768). USA: IEEE.

Scopus Eid


  • 2-s2.0-79959405681

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1816&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/805

Has Global Citation Frequency


Start Page


  • 1764

End Page


  • 1768

Place Of Publication


  • USA

Abstract


  • This paper presents an efficient training approach for support vector machines that will improve their ability to learn from a large or imbalanced data set. Given an original training set, the proposed approach applies unsupervised learning to extract a smaller set of salient training exemplars,

    which are represented by weighted cluster centers and the target outputs. In subsequent supervised learning, the objective function is modified by introducing a weight for each new training sample and the corresponding penalty term. In this paper, we investigate two methods of defining the weight based

    on cluster vectors. The proposed SVM training is implemented and tested on two problems: (i) gender classification of facial images using the FERET data set; (ii) income prediction using the UCI Adult Census data set. Experiment results show that compared to standard SVM training, the proposed approach leads to much faster SVM training, produces a more compact classifier while maintaining generalization ability.

Publication Date


  • 2010

Citation


  • Nguyen, G. Hoang., Phung, S. & Bouzerdoum, A. (2010). Efficient SVM training with reduced weighted samples. IEEE World Congress on Computational Intelligence (pp. 1764-1768). USA: IEEE.

Scopus Eid


  • 2-s2.0-79959405681

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1816&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/805

Has Global Citation Frequency


Start Page


  • 1764

End Page


  • 1768

Place Of Publication


  • USA