重庆大学学报：自然科学版2012，Vol.35Issue(12)：120-132,13.
剔除支持向量回归中异常数据算法
Algorithm of removing outliers in SVR
摘要
Abstract
The outlier and the measurement that an outlier does not fit the theoretical model in the regression problems are defined.The relationship between the theoretical model and the regression model in the regression problem is analyzed.An approximate theorem is proposed and verified by deleting outlier one by one to construct SVR to approximate the theoretical model.An algorithm of detecting outliers in the SVR problems is constructed based on the approximate theorem.The theoretical analysis of the convergence and effectiveness of the proposed algorithm is given.Then,the step-by-step search algorithm is introduced to improve the outlier removing algorithm to remove outliers in SVR with large-scale samples.The theoretical analysis shows that the improved algorithm is convergent and effective.Finally,the samples produced by two test functions and the samples in UCI data set are used for simulation,and the results show that the proposed algorithm is effective and robust.关键词
支持向量回归/异常数据/剔除异常数据算法/仿真Key words
SVR(support vector regression)/algorithm/algorithm of detecting outliers/simulation分类
信息技术与安全科学引用本文复制引用
曾绍华,魏延,唐远炎..剔除支持向量回归中异常数据算法[J].重庆大学学报：自然科学版,2012,35(12):120-132,13.基金项目
重庆市教委科学技术研究项目 （）
重庆市自然科学基金资助项目 （）
