DocumentCode
1933746
Title
Study on SVM On-Line Function Regression Method for Mass Data
Author
An, Jin-long ; Yang, Qing-Xin ; Ma, Zhen-ping
Author_Institution
Hebei Univ. of Technol., Tianjin
Volume
5
fYear
2007
fDate
19-22 Aug. 2007
Firstpage
2773
Lastpage
2777
Abstract
In order to overcome the problems that the SVM training time is too long for a large number of samples and that SVM cannot be trained online when the samples increase dynamically, a new approach of SVM online function regression for mass samples is put forward in this paper. And the validity of this method is proved by simulation experiment.
Keywords
learning (artificial intelligence); regression analysis; support vector machines; SVM online function regression; SVM training; mass data; support vector machine; Constraint optimization; Cybernetics; Electromagnetic fields; Equations; Function approximation; Least squares approximation; Least squares methods; Machine learning; Quadratic programming; Support vector machines; Function regression; Online; Support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-0973-0
Electronic_ISBN
978-1-4244-0973-0
Type
conf
DOI
10.1109/ICMLC.2007.4370619
Filename
4370619
Link To Document