%0 Journal Article
%T A Simplification Algorithm to Support Vector Machines for Regression<br>回归型支持向量机的简化算法
%A TIAN Sheng-feng
%A HUANG Hou-kuan
%A <br>田盛丰
%A 黄厚宽
%J 软件学报
%D 2002
%I 
%X Aiming at the computational complexity resulted from the large amounts of support vectors when the support vector machines (SVMs) are used in function estimation, a simplification algorithm is presented to reduce the number of support vectors and simplify applications. By the adaptation of the simplification algorithm, the LS-SVM (least square support vector machine) algorithm can be combined with SMO (sequential minimal optimization) algorithm to achieve good results with high learning efficiency and a few number of support vectors.
%K support vector machine
%K regression
%K machine learning
%K computational complexity
%K algorithm<br>支持向量机
%K 回归
%K 机器学习
%K 计算复杂性
%K 算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=D2DA9F4546A5BC59&yid=C3ACC247184A22C1&vid=FC0714F8D2EB605D&iid=B31275AF3241DB2D&sid=827D3389B7A27A64&eid=B9196C90508452FE&journal_id=1000-9825&journal_name=软件学报&referenced_num=14&reference_num=5