Support vector set selection using pulse-coupled neural networks

Yunxia Li, Zhang Yi, Jian Cheng Lv

Abstract: A candidate set of support vectors is selected by using pulse-coupled neural networks to reduce computational cost in learning phase for support vector machines (SVMs). The size of the candidate set of support vectors selected this way is smaller than that of the original training samples so that the computation complexity in learning process for support vectors machines based on this candidate set is reduced and the learning process is accelerated. On the other hand, the candidate set of support vectors includes almost all support vectors, and the performance of the SVM based on this candidate set matches the performance when the full training samples are used.