Sample set shrinking strategy efficiently improving parameters seeking of support vector machines
有效提高SVM参数搜索效率的样本集缩减策略

The choice of kernel function and relative parameters plays an important role in Support Vector Machines (SVMs). It greatly influences the generalization performance of SVMs. It is time consuming to seek for optimal parameters when the training sample set is large. Concerning this problem, a sample set shrinking strategy was proposed. This method took some of the non-support-vector samples out of the training set; therefore efficiently reduced the set size. That is to say, with half the time consumed, a model can be constructed with testing accuracy just slightly changed.