کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
536515 | 870547 | 2011 | 6 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Selecting training points for one-class support vector machines Selecting training points for one-class support vector machines](/preview/png/536515.png)
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.
► A novel training points selection method based on local geometry and kNN is developed.
► It selects extreme points which sit on the boundary of data distribution.
► It increases training efficiency and maintains generalisation capability of 1-class SVM.
Journal: Pattern Recognition Letters - Volume 32, Issue 11, 1 August 2011, Pages 1517–1522