Kernel Subclass Convex Hull Sample Selection Method and Its Application on SVM
Jingyu Yang · Jisuanji gongcheng · 2008
A novel intra-class sample selection method named kernel subclass convex hull sample selection algorithm is proposed and used for SVM.The algorithm is an iterative procedure based on kernel trick.At each step,only one sample furthest to the convex hull spanned by chosen samples is picked out in the feature space.Experiments show that a significant amount of training data can be removed without sacrificing the performance of SVM,while the memory requirements and the computation time of the classifiers are reduced significantly.