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

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.