计算机工程与应用Issue(23)：140-144,162,6.DOI:10.3778/j.issn.1002-8331.1301-0136
最小二乘双支持向量回归机
Least square twin support vector regression
摘要
Abstract
In this paper Least Square Twin Support Vector Regression(LSTSVR)is proposed, which is formulated via the idea of Twin Support Vector Regression(TSVR). LSTSVR breaks the idea which ε-band is constructed by two parallel hyperplanes in traditional Support Vector Regression(SVR). Actually, LSTVR employes two non-parallel hyperplanes to construct the ε-band, in which each hyperplane determinates a half ε-bond, and obtain the final regression. So the regression function fits the distribution of dataset and the algorithm has better generalization ability. In addition, in LSTSVR, the main computing cost is to solve two smaller systems of linear equations, so the computational complexity is low. The experimental results indicate that the proposed algorithm has certain advantage in generalization ability and computational efficiency.关键词
回归问题/支持向量回归机/双支持向量回归机/最小二乘双支持向量回归机Key words
regression problem/support vector regression/twin support vector regression/least square twin support vector regression分类
数理科学引用本文复制引用
卢振兴,杨志霞,高新豫..最小二乘双支持向量回归机[J].计算机工程与应用,2014,(23):140-144,162,6.基金项目
国家自然科学基金（No.11161045）。 （）
