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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.

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Abstract

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: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Pattern Recognition Letters
Publisher: Elsevier B.V.
Refereed: Yes
Related URLs:
Funders: na
Depositing User: Yuhua Li
Date Deposited: 20 Aug 2015 16:31
Last Modified: 05 Apr 2016 18:18
URI: http://usir.salford.ac.uk/id/eprint/33116

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