A Novel Sample Reduction Method for Support Vector Regression Based on Memory Mode
Aims to solving the problem of training speed and memory taking during traditional support vector regression (SVR) training for large scale sample sets, a method based on memory mode is proposed in this paper, named memory mode support vector regression (MM-SVR). By simulating the memory law of human with a forgetting factor and considering the importance of data to simulating actual physical process, the offline history data is sampled by utilizing the timeliness of the observation data. The sampled data is taken as the training set, on which the model was gained by support vector regression. The simulation tests are carried out on several benchmark datasets. The results show that MM-SVR has advantages compared to RS-SVR and original SVR on training speed and robustness.
Support Vector Regression Memory Mode Forgetting Factor Sample
HUANG Jingtao LUO Wei REN Zhiwei JIANG Aipeng
Electronic & Information Engineering College, Henan University of Science & Technology, Luoyang 4710 School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
7119-7124
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）