基于二分网格的支持向量预选取算法  

Pre-selecting support vectors based on bisection grid

作　　者：唐耀华[1] 高静怀[1] 邱保志[1] 

机构地区：[1]西安交通大学电子与信息工程学院,西安710049

出　　处：《控制与决策》2007年第7期755-759,764,共6页Control and Decision

基　　金：国家自然科学基金项目(40174032).

摘　　要：在SVM训练过程中,二次规划问题的求解制约着SVM应用于大规模数据.SVM的决策函数由邻近分类超平面的部分训练样本——支持向量决定.基于减小训练样本数目、加快SVM训练过程的目的,提出一种基于二分网格的边界样本提取方法.数据仿真实验表明,该方法具有边界样本提取准确、效率高、速度快、能够自适应样本分布的优点,而且不会显著降低SVM分类器的性能.In training a SVM, the solution of a quadratic programming （QP） problem constrains the SVM in large scale problem. The decision ruction of SVM is determined by a subset of the training patterns, support vectors, which lies closer to the classification hyperplane. Motivated by reducing the training patterns, a grid-based algorithm is proposed to pre-select a subset of patterns near the decision boundary. Simulation experiments show that BS-grid efficiently extracts accurate boundary patterns without degrading the classification ability of SVM and has good adaptive capacity to pattern＇s density and outline.

关 键 词：支持向量机 二分网格 分类 边界样本 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]