Li, Yuhua (2011) Selecting training points for one-class support vector machines. Pattern Recognition Letters, 32 (11). pp. 1517-1522. [Journal article]
Full text not available from this repository.
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
|Deposited By:||Dr Yuhua Li|
|Deposited On:||01 Aug 2011 14:22|
|Last Modified:||01 Aug 2011 14:22|
Repository Staff Only: item control page