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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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.
|Schools:||Schools > School of Computing, Science and Engineering|
|Journal or Publication Title:||Pattern Recognition Letters|
|Depositing User:||Yuhua Li|
|Date Deposited:||20 Aug 2015 16:31|
|Last Modified:||05 Apr 2016 18:18|
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