id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_4e2xp7dgsrfatbouk5aoymocqi	D KIM	Response modeling with support vector regression	2008	17	.pdf	application/pdf	3802	468	73	applied Support Vector Regression (SVR) for response modeling to predict total results of a direct marketing dataset in terms of model fit, training time complexity Key words: Response Modeling, Direct Marketing, Support vector machines; As the number of training patterns increases, SVR training takes much longer with a time complexity of O(N 3) where N denotes the In this paper, we applied SVR for response modeling to predict total amount after predicting respondents by a classification response model, a regression SVR is moved around to include training patterns inside ε-insensitive tube SVR trains patterns based on ε-loss function foundation. 7. Train final SVR with s selected patterns pattern selection (SVR-PS) while the lines with boxes presents the results of selected patterns was up to 40%, which resulted only 5 dollars' gap, SVR-PS setting, the response model predicted the dollar amount to each test customer. Kim, D., Cho, S., 2006.ε-tube based Pattern Selection for Support Vector	./cache/work_4e2xp7dgsrfatbouk5aoymocqi.pdf	./txt/work_4e2xp7dgsrfatbouk5aoymocqi.txt