id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_othruimflnaupb3s56fqcmitji	Horng-Lin Shieh	A reduced data set method for support vector regression	2010	3	.pdf	application/pdf	1868	221	73	A reduced data set method for support vector regression A reduced data set method for support vector regression When there is noise and/or outliers exist in sampling data, the SVR may try to fit those improper data, and obtained systems may have In this paper, a reduced support vector regression is proposed for nonlinear function approximation problems with noise and outliers. approach is to adopt fuzzy clustering and a robust fuzzy c-means (RFCM) algorithm to reduce the computational time of SVR and greatly mitigates the influence of data noise and outliers. Training of the SVM involves optimization of a convex cost function and globally minimizes to complete the learning process (Campbell, 2002). fuzzy clustering method is proposed to greatly mitigate the influence of noise and outliers in sampling data, and then the SVR The model of learning from examples can be considered a general statistic framework of minimizing expected loss using sampling data.	./cache/work_othruimflnaupb3s56fqcmitji.pdf	./txt/work_othruimflnaupb3s56fqcmitji.txt