%0 Journal Article
%T Incorporating Prior Knowledge into Kernel Based Regression<br>先验知识与基于核函数的回归方法的融合
%A SUN Zhe
%A ZHANG Zeng-Ke
%A WANG Huan-Gang
%A <br>孙喆
%A 张曾科
%A 王焕钢
%J 自动化学报
%D 2008
%I 
%X In some sample based regression tasks,the observed samples are quite few or not informative enough.As a result,the conflict between the number of samples and the model complexity emerges,and the regression method will confront the dilemma whether to choose a complex model or not.Incorporating the prior knowledge is a potential solution for this dilemma.In this paper,a sort of the prior knowledge is investigated and a novel method to incorporate it into the kernel based regression scheme is proposed.The proposed prior knowledge based kernel regression(PKBKR)method includes two subproblems:representing the prior knowledge in the function space,and combining this representation and the training samples to obtain the regression function.A greedy algorithm for the representing step and a weighted loss function for the incorporation step are proposed.Finally,experiments are performed to validate the proposed PKBKR method,wherein the results show that the proposed method can achieve relatively high regression performance with appropriate model complexity,especially when the number of samples is small or the observation noise is large.
%K Machine learning
%K prior knowledge
%K kernel based regression
%K iterative greedy algorithm
%K weighted loss function<br>计算方法
%K 回归方程
%K 机械学习
%K 自动化系统
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=8F858F98E2D27B2DDB23B03CA44FF635&yid=67289AFF6305E306&vid=339D79302DF62549&iid=59906B3B2830C2C5&sid=6F185A924223F19E&eid=ECD12A4C29B53D67&journal_id=0254-4156&journal_name=自动化学报&referenced_num=1&reference_num=19