Sample Reduction Strategy for Support Vector Machines with Large-Scale Data Set
大规模数据集下支持向量机训练样本的缩减策略

Training algorithm for large-scale support vector machines(SVM) is an important and active subject in the field of SVM research.After the analysis of the nature and difficulties in training SVM,a new reduction strategy is proposed in this paper for training svm with large-scale sample set.In general,class centroid is solved before training and removing the samples corresponding to non support vectors.Through this method,the number of samples is reduced before training svm.This method is fast in convergence without accurate loss and propose the explanation of SVM theory from space geometry.The re- sults of simulation experiments show the feasibility and effectiveness of this method.