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Dataset selection for training one-class support vector machines

Li, Y and Maguire, L 2009, Dataset selection for training one-class support vector machines , in: International Conference on Computational Intelligence and Software Engineering, 11-13 December 2009, Wuhan, China.

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Abstract

This paper proposes an efficient training strategy for one-class support vector machines. The strategy exploits the feature of a trained one-class SVM which uses points only residing on the exterior region of data distribution as support vectors. Thus the proposed training set reduction method selects the so-called extreme points which sit on the boundary of data distribution, through local geometry and k-nearest neighbors. Experimental results on synthetic and real-world data demonstrate that the proposed training strategy can reduce training set of support vector machines considerably while the obtained model maintains generalization capability to the level of a model trained on the full training set.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Computational Intelligence and Software Engineering, 2009, CiSE 2009, International Conference
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 27 Jul 2015 10:59
Last Modified: 05 Apr 2016 18:18
URI: http://usir.salford.ac.uk/id/eprint/33123

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