计算机技术与发展Issue(6)：87-91,5.DOI:10.3969/j.issn.1673-629X.2015.06.019
基于MapReduce的SVM分类算法研究
Research on SVM Classification Algorithm Based on MapReduce
摘要
Abstract
In cloud computing environment,the method adopted by the traditional SVM sorting algorithms based on MapReduce of train-ing data set is too simple and it just merges support vectors after nodes’ training,so the efficiency and accuracy of classifier are not very ideal. To solve the problem above,an improved training algorithm is proposed in this paper. Firstly,use the genetic algorithm to get the optimal kernel function and parameters on each node at the same time,then using the combination to train the data set for support vector, and afterwards,combining all support vectors after training as a global support vector,and then merging every data subset with global sup-port vector on each node to get a new training data set. Repeat these four steps until the global support vector no longer changes and that’ s to say,it converges to the optimal classification model. Finally,the experiment on Hadoop proves that the classification accuracy of new algorithm is improved obviously than traditional classification algorithms.关键词
MapReduce/SVM分类算法/遗传算法/云计算Key words
MapReduce/SVM sorting algorithm/genetic algorithm/cloud computing分类
信息技术与安全科学引用本文复制引用
秦军,戴新华,童毅,林巧民..基于MapReduce的SVM分类算法研究[J].计算机技术与发展,2015,(6):87-91,5.基金项目
江苏省自然科学基金项目(BK20130882) （BK20130882）
