用双层减样法优化大规模SVM垃圾标签检测模型*

针对支持向量机在训练大规模数据集时出现的速度瓶颈问题，提出一种新的减样方法，称双层减样法。数据减样时，双层减样法从粗、细粒度两个层次削减样本。粗粒度约减时，利用核空间距离聚类法，以簇为单位削减冗余子集。细粒度约减时，以点为单位挑选剩余点集中的支持向量。实验表明，双层减样法能有效的压缩样本数据，同时还能放大数据集的分类特征，提高分类器的分类精度。将此法应用于大规模SVM垃圾标签检测模型的训练集优化上，能明显提高检测模型的训练速度。双层减样法是将“粒度”和“层次”的概念引入减样法中，在约减时适时改变约减幅度。这比传统减样法更具有优势。

Double-layer reduction method optimizes large scale SVM social spam detection model

In order to improve the low efficiency of large-scale SVM,this paper presented a new samples reduction method,called double-layer reduction method.It reduced data in two levels.The first level was coarse-grained reduction.It deleted the redundant clusters with KDC reduction.The second level was fine-grained reduction.It picked out the support vectors from the clusters remained by SMO.The experiments show that double-layer reduction method gives a higher compression ratio and accuracy.It applied the new meth...