基于MTC结构的支持向量机并行训练算法  

A Parallel Training Algorithm of Support Vector Machines Based on the MTC Architecture

作　　者：贾华丁[1] 游志胜[1] 王磊[2] 

机构地区：[1]四川大学计算机学院,四川成都610064 [2]西南财经大学经济信息工程学院,四川成都610074

出　　处：《四川大学学报（工程科学版）》2007年第6期123-128,共6页Journal of Sichuan University (Engineering Science Edition)

基　　金：国家自然科学基金资助项目(69732010)

摘　　要：为加快支持向量机的训练速度,提出一种新型的"多重三叉级联(MTC)"学习结构,具有反馈速度快、计算节点利用率高、反馈的支持向量多等优点。基于该结构设计了支持向量机的并行训练算法,并严格证明了新算法能够收敛到支持向量机的最优解。数值实验结果表明,新算法具有非常高的加速比和并行效率,需要的训练时间显著地少于Graf等提出的Cascade SVM算法。For accelerating the training speed of support vector machines （ SVM）, a novel ＂multi-trifurcate cascading （MTC）＂ architecture, which held the advantages of fast feedback, high utilization rate of nodes, and more feeding support vectors,was proposed. A parallel algorithm for training SVM was designed based on the MTC architecture, and it was proven to converge to the optimal solution strictly. The experimental results showed that the proposed algorithm obtained very high speedup and efficiency, and needed significantly less training time than the Cascade SVM algorithm.

关 键 词：支持向量机 训练算法 并行学习结构 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]