计算机工程与应用2017，Vol.53Issue(3)：169-173,5.DOI:10.3778/j.issn.1002-8331.1507-0245
基于边界样本选择的支持向量机加速算法
SVM accelerated training algorithm based on border sample selection
摘要
Abstract
Support Vector Machine(SVM)is a powerful instrument for solving pattern classification problem, but it is not suitable for large-scale data, due to the drawbacks of slow training speed, large computational cost and low generalization. An accurate support vector machine algorithm is proposed, which uses training samples lying close to the separation boundary. First of all, K-means clustering is performed to the initial training data, and then the boundary samples are se-lected in each cluster by K-nearest neighbor algorithm, two cluster factors, the degree of mixing and support, are defined to determine the boundary width. These boundary samples are then used in the training of the SVM classifier. The experi-ments on some benchmark datasets show that the proposed method not only makes computational complexities decreased, but also makes classification power of traditional SVM invariant.关键词
支持向量机/大规模分类/边界样本/聚类Key words
Support Vector Machine(SVM)/large-scale classification/boundary samples/clustering分类
信息技术与安全科学引用本文复制引用
胡小生,钟勇..基于边界样本选择的支持向量机加速算法[J].计算机工程与应用,2017,53(3):169-173,5.基金项目
2014年国家星火计划项目(No.2014GA780031) （No.2014GA780031）
广东省自然科学基金(No.2015A030313638) （No.2015A030313638）
广东高校优秀青年创新人才培养计划资助项目(No.2013LYM_0097,No.2014KQNCX184) （No.2013LYM_0097,No.2014KQNCX184）
佛山科学技术学院校级科研项目. （）
