南京理工大学学报（自然科学版）2011，Vol.35Issue(1)：138-143,6.
基于梯度信息的最小二乘支持向量回归机
Gradient-enhanced Least Squares Support Vector Regression
摘要
Abstract
To solve the problem of the larger number of samples being required to improve the regression accuracy in the least squares support vector regressions (LS-SVR), a model of gradient-en hanced least squares support vector regression (GE-LSS-VR)is proposed. After changing the objective functions and constraint conditions, the gradient is introduced into the model, and the decision function is reconstructed. Three benchmark functions are used to verify the model. Three commonly-used measurement criterions are used to compare the experimental results. The results show that the model presented here can achieve an ideal regression accuracy at the cost of smaller samples.关键词
支持向量机/最小二乘支持向量回归机/梯度信息/计算机试验Key words
support vector machine/ least squares support vector regression/ gradient/ computer experiments分类
信息技术与安全科学引用本文复制引用
周晓剑,马义中,刘利平,汪建均..基于梯度信息的最小二乘支持向量回归机[J].南京理工大学学报（自然科学版）,2011,35(1):138-143,6.基金项目
国家自然科学基金重点项目(70931002) （70931002）
国家自然科学基金(70672088) （70672088）
