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Selecting training points for one-class support vector machines

Li, Yuhua (2011) Selecting training points for one-class support vector machines. Pattern Recognition Letters, 32 (11). pp. 1517-1522. [Journal article]

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URL: http://dx.doi.org/10.1016/j.patrec.2011.04.013

DOI: 10.1016/j.patrec.2011.04.013


This paper proposes a training points selection method for one-class support vector machines. It 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 neighbours. Experimental results demonstrate that the proposed method can reduce training set considerably, while the obtained model maintains generalization capability to the level of a model trained on the full training set, but uses less support vectors and exhibits faster training speed.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:19278
Deposited By: Dr Yuhua Li
Deposited On:01 Aug 2011 14:22
Last Modified:01 Aug 2011 14:22

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