信息与控制2016，Vol.45Issue(4)：432-436,443,6.DOI:10.13976/j.cnki.xk.2016.0432
基于参数间隔孪生支持向量机的增量学习算法
Incremental Learning Method Based on Twin Parametric-margin Support Vector Machine
杨海涛 1肖军 1王佩瑶 1王威2
作者信息
- 1. 辽宁石油化工大学信息与控制工程学院，辽宁抚顺 113001
- 2. 中国石油天然气股份有限公司辽河石化分公司仪电运行部
- 折叠
摘要
Abstract
Although the amount of massive time series data or data flow has continued to increase,the classification training speed of the twin parametric-margin support vector machine (TPMSVM)is still very slow.This study provides proof of the sample satisfying the KKT condition of the TPMSVMcorresponding to the numerical con-dition.According to the conclusion,an incremental learning algorithm enabling the TPMSVM to deal with time series data is proposed.This algorithm selects the new data that violate the generalized KKT condition and the partial original data that satisfies the condition to participate in the classifier training.The experimen-tal results show that the proposed algorithm not only maintains a certain classification accuracy but can also improve the accuracy of the TPMSVM training.关键词
参数间隔孪生支持向量机/广义KKT条件/增量学习/时间序列数据Key words
twin parametric-margin support vector machine (TPMSVM)/generalized KKT (Karush-Kuhn-Tucker)/condition incremental learning/time series data分类
信息技术与安全科学引用本文复制引用
杨海涛,肖军,王佩瑶,王威..基于参数间隔孪生支持向量机的增量学习算法[J].信息与控制,2016,45(4):432-436,443,6.
