基于错分样本的AdaBoost支持向量预选取算法  

The Support Vector Pre-extracting Method Based on Error Samples AdaBoost Algorithm

作　　者：叶菲 罗军[1] 

机构地区：[1]解放军陆军军官学院,安徽合肥230031

出　　处：《微电子学与计算机》2013年第4期50-52,共3页Microelectronics & Computer

摘　　要：支持向量机在大样本情况下训练速度慢,支持向量预选取可以解决这个问题.AdaBoost算法重点关注错分样本,而错分样本一般都处于分类边界,支持向量就由分类边界样本构成.因此,提出基于错分样本的AdaBoost支持向量预选取算法,该算法通过AdaBoost提升过程,使得越是容易被错分的样本权值越大,从而实现支持向量的预选取,通过仿真实验验证了算法的有效性.SVM has slow training speed in the condition of a great deal of samples, and support vector Pre-extracting can solve this problem. Adaboost algorithm emphasizes error samples, and error samples are boundary samples which can construct support vector. So the support vector pre-extracting method based on error samples AdaBoost algorithm is proposed. Through AdaBoost upgrading process the algorithm gets error smples a big weight, and the support vector pre-extracting carries out, in the end the simulation experiments validate the algorithm is effective.

关 键 词：ADABOOST 错分样本 SVM 支持向量预选取 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]