文献类型：期刊文献

英文题名：Online Learning Algorithms for Double-Weighted Least Squares Twin Bounded Support Vector Machines

作者：Li, Juntao[1];Cao, Yimin[1];Wang, Yadi[1];Xiao, Huimin[2]

第一作者：Li, Juntao

通讯作者：Li, JT[1]

机构：[1]Henan Normal Univ, Sch Math & Informat Sci, Henan Engn Lab Big Data Stat Anal & Optimal Contr, Xinxiang 453007, Peoples R China;[2]Henan Univ Econ & Law, Coll Comp & Informat Engn, Zhengzhou 450002, Peoples R China

第一机构：Henan Normal Univ, Sch Math & Informat Sci, Henan Engn Lab Big Data Stat Anal & Optimal Contr, Xinxiang 453007, Peoples R China

通讯机构：[1]corresponding author), Henan Normal Univ, Sch Math & Informat Sci, Henan Engn Lab Big Data Stat Anal & Optimal Contr, Xinxiang 453007, Peoples R China.

年份：2017

卷号：45

期号：1

起止页码：319-339

外文期刊名：NEURAL PROCESSING LETTERS

收录：;EI(收录号：20162002397690);Scopus(收录号：2-s2.0-84966703852);WOS:【SCI-EXPANDED(收录号:WOS:000394336300019)】；

基金：The authors would like to thank the anonymous reviewers for their valuable comments and insightful suggestions. The authors are also thankful to National Natural Science Foundation of China (61203293, 61374079), Key Scientific and Technological Project of Henan Province (122102210131), Program for Science and Technology Innovation Talents in Universities of Henan Province (13HASTIT040), Program for Innovative Research Team (in Science and Technology) in University of Henan Province (14IRTSTHN023), Henan Higher School Funding Scheme for Young Teachers (2012GGJS-063). Backbone Teachers Program of Henan Normal University.

语种：英文

外文关键词：Support vector machine; Twin bounded support vector machine; Double-weighted mechanism; Online learning algorithms; Pruning mechanism

摘要：Twin support vector machine with two nonparallel classifying hyperplanes and its extensions have attracted much attention in machine learning and data mining. However, the prediction accuracy may be highly influenced when noise is involved. In particular, for the least squares case, the intractable computational burden may be incurred for large scale data. To address the above problems, we propose the double-weighted least squares twin bounded support vector machines and develop the online learning algorithms. By introducing the double-weighted mechanism, the linear and nonlinear double-weighted learning models are proposed to reduce the influence of noise. The online learning algorithms for solving the two models are developed, which can avoid computing the inverse of the large scale matrices. Furthermore, a new pruning mechanism which can avoid updating the kernel matrices in every iteration step for solving nonlinear model is also developed. Simulation results on three UCI data with noise demonstrate that the online learning algorithm for the linear double-weighted learning model can get least computation time as well considerable classification accuracy. Simulation results on UCI data and two-moons data with noise demonstrate that the nonlinear double-weighted learning model can be effectively solved by the online learning algorithm with the pruning mechanism.