Abstract:

To accelerate the training of support vector domain description (SVDD), confidence support vector domain description (CSVDD) is proposed based on the observation that the description boundary is determined by a small subset of training data called support vectors. Namely, the number of training samples in the userdefined sphere is calculated and taken as the confidence measure, according to which the training samples are ranked in ascending order. Those former ranked ones are selected as the boundary targets for the SVDD training. Simulations on UCI data demonstrate the effectiveness and superiority of CSVDD: the number of training targets and the training time are reduced without any loss of accuracy.