Selecting Support Vector Candidates for Incremental Training
Shinya Katagiri, Shigeo Abe · 2006
In the conventional incremental training of support vector machines (SVMs), candidates of support vectors tend to be deleted if the separating hyperplane rotates as the training data are added. To solve this problem, in this paper, we propose an incremental training method using one-class support vector machines. First, we generate a hypersphere for each class. Then, we keep data that exist near the boundary of the hypersphere as candidates of support vectors and delete others. By computer simulations for two-class benchmark data sets, we show that we can robustly delete data considerably without deteriorating the generalization ability.