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.

[img] PDF
Restricted to Repository staff only

Download (464kB)


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

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)


Downloads per month over past year