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Presentation
Efficient SVM training with reduced weighted samples
Faculty of Informatics - Papers (Archive)
  • Son Lam Phung, University of Wollongong
  • Giang Hoang Nguyen, University of Wollongong
  • Abdesselam Bouzerdoum, University of Wollongong
RIS ID
33595
Publication Date
1-1-2010
Publication Details

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

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.
Citation Information
Son Lam Phung, Giang Hoang Nguyen and Abdesselam Bouzerdoum. "Efficient SVM training with reduced weighted samples" (2010) p. 1764 - 1768
Available at: http://works.bepress.com/son_lam_phung/9/