[1]周晓剑,马义中,刘利平,等.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,(01):138-143.
 ZHOU Xiao-jian,MA Yi-zhong,LIU Li-ping,et al.Gradient-enhanced Least Squares Support Vector Regression[J].Journal of Nanjing University of Science and Technology,2011,(01):138-143.
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基于梯度信息的最小二乘支持向量回归机
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
期数:
2011年01期
页码:
138-143
栏目:
出版日期:
2011-02-28

文章信息/Info

Title:
Gradient-enhanced Least Squares Support Vector Regression
作者:
周晓剑马义中刘利平汪建均
南京理工大学经济管理学院,江苏南京210094
Author(s):
ZHOU Xiao-jianMA Yi-zhongLIU Li-pingWANG Jian-jun
School of Economics and Management,NUST,Nanjing 210094,China
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
Keywords:
support vector machine least squares support vector regression gradient computer experiments
分类号:
TP18
摘要:
为了解决传统最小二乘支持向量回归机( LS-SVR) 对训练样本量要求过高的问题,提出 了基于梯度信息的支持向量回归机( GE-LS-SVR) 模型。通过修改目标函数及约束条件,将梯度 信息引入模型的构建中,重新构造了决策函数。采用了三个基准函数对模型进行了验证,并用 三个常用度量准则对实验结果进行了比较。结果表明提出的模型能在较少样本的情况下达到 较为理想的回归精度。
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-enhanced 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.

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备注/Memo

备注/Memo:
基金项目:国家自然科学基金重点项目( 70931002) ; 国家自然科学基金( 70672088) 作者简介:周晓剑( 1979-) ,男,博士生,主要研究方向:人工智能、智能质量控制,E-mail: xjzhou2008@ yahoo. com. cn; 通讯作者:马义中( 1964-) ,男,教授,博士生导师,主要研究方向: 质量工程、质量管理,E-mail: yzma-2004 @163. com。
更新日期/Last Update: 2012-02-28