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
Funders:	na
Depositing User:	Yuhua Li
Date Deposited:	20 Aug 2015 16:31
Last Modified:	15 Feb 2022 15:47
URI:	https://usir.salford.ac.uk/id/eprint/33116

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