%0 Journal Article
%T Least Square Generalized Support Vector Machines for Regression<br>用于回归估计的最小二乘广义支持向量机
%A SUN Zong-hai
%A SUN You-xian
%A <br>孙宗海
%A 孙优贤
%J 系统工程理论与实践
%D 2004
%I 
%X Least square generalized support vector machines (LS__GSVMs) are applied to regression estimation. LS__GSVMs' kernel functions have no or few limits when they are compared with standard support vector machines (SVMs). We give a presentation of quadratic programming (QP) problem for the LS__GSVMs. In order to solve the QP problem, we apply the combination of the gradient projection and successive overrelaxation (SOR) based on the matrix splitting. That is, we train the LS__GSVMs with above algorithm. Because SOR handles one point at a time, it can process very large datasets that need not reside in memory.
%K least square generalized support vector machines
%K regression estimation
%K successive overrelaxation
%K matrix splitting<br>最小二乘广义支持向量机
%K 回归估计
%K 超松弛法
%K 矩阵分裂
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=01BA20E8BA813E1908F3698710BBFEFEE816345F465FEBA5&cid=962324E222C1AC1D&jid=1D057D9E7CAD6BEE9FA97306E08E48D3&aid=2CA9FE864AC45EE4&yid=D0E58B75BFD8E51C&vid=B91E8C6D6FE990DB&iid=DF92D298D3FF1E6E&sid=BB0EA31DB1B01173&eid=C3BF5C58156BEDF0&journal_id=1000-6788&journal_name=系统工程理论与实践&referenced_num=3&reference_num=7