%0 Journal Article
%T SVR with Adaptive Error Penalization<br>自适应误差惩罚支撑向量回归机
%A Chen Xiao-feng
%A Wang Shi-tong
%A Cao Su-qun
%A <br>陈晓峰
%A 王士同
%A 曹苏群
%J 电子与信息学报
%D 2008
%I 
%X A novel support vector regression method AEPSVR is proposed in this paper. First, an approximate regression function is obtained using -SVR method, and then a new adaptive error penalization function is introduced to enhance the robust performance of SVR such that a robust support vector regression is derived. Because the proposed AEPSVR here is based on -SVR, so various optimization methods for SVR can be used. Experimental results show that the proposed AEPSVR can reduce the affect of outliers, and have the very good generalization capability.
%K Support Vector Regression (SVR)
%K Outlier
%K Adaptive Error Penalization (AEP)<br>支撑向量回归
%K 离群点
%K 自适应误差惩罚
%K 自适应
%K 误差
%K 惩罚函数
%K 支撑向量回归机
%K Penalization
%K Error
%K 泛化性能
%K 影响
%K 离群点
%K 实验
%K 优化算法
%K 求解
%K 应用
%K 方法
%K 鲁棒
%K 迭代
%K 回归函数
%K 近似
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=69527733A53A82BE61B7F8559F82090D&yid=67289AFF6305E306&vid=340AC2BF8E7AB4FD&iid=0B39A22176CE99FB&sid=4C2B9916B58305BE&eid=0918129209B14F3E&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=12