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
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;
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
DOI :
10.1109/ICMLC.2007.4370619
