NDLI: A novel sample reduction method for support vector regression ased on memory mode

Content Provider	IEEE Xplore Digital Library
Author	Huang Jingtao Luo Wei Ren Zhiwei Jiang Aipeng
Copyright Year	2012
Description	Author affiliation: Electronic & Information Engineering College, Henan University of Science & Technology, Luoyang 471003, China (Huang Jingtao; Luo Wei; Ren Zhiwei) \|\| School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China (Jiang Aipeng)
Abstract	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.
Starting Page	7119
Ending Page	7124
File Size	287014
Page Count	6
File Format	PDF
ISBN	9781467325813
ISSN	21612927
e-ISBN	9789881563811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Support vector machines Training Forgetting Factor Memory Mode Sample Support Vector Regression Predictive models Sampling methods Mathematical model Kernel Standards
Content Type	Text
Resource Type	Article

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