Support vector quantile regression using asymmetric e-insensitive loss function

摘要：

Support vector quantile regression (SVQR) is capable of providing a good description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse SVQR to overcome a weak point of SVQR, nonsparsity. The asymmetric einsensitive loss function is used to efficiently provide the sparsity Experimental results are then presented; these results illustrate the performance of the proposed method by comparing it with nonsparse SVQR.

关键词： quantile regression sparsity support vectors support vector quantile regression

作者: Kyung Ha Seok Daehyeon Cho Changha Hwang Jooyong Shim

作者单位: Department of Data Science and Institute of Statistical Information lnje University Kimhae, 621-749, Department of Statistics Dankook University Yongin-si, Gyeonggi-do, 448-701, Korea Department of Applied Statistics Catholic University of Daegu Gyungbuk, 702-701, Korea

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

会议地点: 上海

会议语种:英文

页码: 438-441

在线出版日期: 2010-06-22（万方平台首次上网日期，不代表论文的发表时间）