%0 Journal Article
%T Study of Least Squares Support Vector Machines<br>最小二乘支持向量机算法研究
%A ZHU Jia-Yuan CHEN Kai-Tao ZHANG Heng-Xi
%A <br>朱家元
%A 陈开陶
%A 张恒喜
%J 计算机科学
%D 2003
%I 
%X In this paper, we present a least squares version for support vector machines(SVM)classifiers and function estimation. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM. The approach is illustrated on a two-spiral benchmark classification problem. The results show that the LS-SVM is an efficient method for solving pattern recognition.
%K Statistical learning theory
%K Support vector machines
%K Pattern Recognition
%K Least squares support vector machines
%K Neural networks<br>支持向量机
%K 机器学习
%K 模式识别
%K 最小二乘算法
%K 函数估计
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=E3140B97F95286E4&yid=D43C4A19B2EE3C0A&vid=340AC2BF8E7AB4FD&iid=DF92D298D3FF1E6E&sid=0B4F496D54044D86&eid=BA79719BCA7341D5&journal_id=1002-137X&journal_name=计算机科学&referenced_num=17&reference_num=8