计算机应用与软件Issue(3)：172-176,5.DOI:10.3969/j.issn.1000-386x.2015.03.040
基于 MapReduce 的层叠分组并行 SVM 算法研究
RESEARCH ON CASCADE-GROUPING PARALLEL SVM ALGORITHM BASED ON MAPREDUCE
摘要
Abstract
With the constant growing of training set scale,support vector machine learning becomes intensive computing process.In view of the problems in calculation process including large memory and slow optimisation,we analyse the performances of two parallel SVM algorithms of grouping training and cascade training through a great deal of experiments,and present the cascade-grouping SVM parallel algorithm,and implement it using MapReduce parallel framework,this solves the problem of low efficiency of cascade training model. Experimental results show that by using this learning strategy,the training time is reduced and the classification speed is improved both to a certain extent without big precision loss.关键词
并行分类算法/支持向量机/MapReduce/大规模数据集处理Key words
Parallel classification algorithm/Support vector machine/MapReduce/Large-scale dataset processing分类
信息技术与安全科学引用本文复制引用
张鹏翔,刘利民,马志强..基于 MapReduce 的层叠分组并行 SVM 算法研究[J].计算机应用与软件,2015,(3):172-176,5.基金项目
国家自然科学基金项目（61363052）；内蒙古自然科学基金项目（2010MS0913）；内蒙古自治区气象信息中心合作项目 （）
