基于随机中心距离排序的支持向量预选取方法  

A Random Center Distance Sorting-based Support Vector Pre-extracted Method

作　　者：胡志军[1] 王鸿斌[1] 李荣[1] 

机构地区：[1]忻州师范学院计算机系,山西忻州034000

出　　处：《微电子学与计算机》2013年第8期36-39,共4页Microelectronics & Computer

基　　金：山西省自然科学基金项目(2009011018-4);山西省高校科技开发项目(20101120)

摘　　要：提出了一种基于随机中心距离排序的支持向量预选取方法.对于线性可分情况,该方法首先从每一个类别中随机选取一定数目的样本计算均值,并把该均值作为该类别样本的随机中心,然后对每一个样本计算它与另一类样本随机中心之间的距离,最后选择一定数目具有较小随机中心距离的原始样本组成边界样本集.对于非线性可分情况,此算法借助于核函数将原始问题映射到特征空间,然后再按照线性可分情况求解.由于支持向量往往分布在两类样本相邻的边界区域,因此此方法可以较为精确地预选取支持向量.在部分UCI标准数据集和ORL人脸数据库上的实验说明此算法较以往支持向量预选取算法可以更为快速准确地进行支持向量预选取.In this paper a random center distance sorting-based support vector pre-extracted method is proposed.For linear separable case,the method firstly randomly selected from each category a certain number of samples and calculated the mean of them as a random center of the category.Then the method calculated for each sample the distance between the sample and the random center of the other class.Finally select a certain number of original samples with small distances to compose boundary samples.For non-linear separable case,the method maps the original problem into feature space with kernel functions,and solves the problem like linear separable case.As support vectors often locate in the border area adjacent to the other type of samples,this method can pre-extract support vectors more exactly.Experiments on UCI standard data sets and ORL data set show that the proposed algorithm can pre-extract support vectors faster and more exactly than previous support vectors pre-extraction methods.

关 键 词：支持向量预选取 随机中心 距离排序 边界样本集 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]