Decremental Learning based on Sample-Weighted Support Vector Regression

摘要：

In this paper, a new modeling method-decremental learning based on sample-weighted SVR(DSWSVR) is proposed, which introduces the decremental learning strategy into sample selection based on support vector regression (SVR). DSWSVR differs from SVR in that it builds a new sample set, where some sample in the original sample set are weighted differently to account for its representative to improve the prediction ability of the algorithm. Simulation results show that the proposed algorithm can improve the performance of the SVR modeling.

关键词： Support Vector Regression (SVR) Decremental Learning Weighted Sample Genetic Algorithm

作者: Li Qing Wang Ling Zhang De Zheng Zhang Wei Cun

作者单位: School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijin

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 1334-1337

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）