|本期目录/Table of Contents|

[1]赵永平,孙健国.基于薄壁管法的稀疏最小二乘支持向量回归机[J].电机与控制学报,2009,(04):581-585.
 ZHAO Yong-ping,SUN Jian-guo.Thin wall tube method based sparse least squares support vector regression[J].,2009,(04):581-585.
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基于薄壁管法的稀疏最小二乘支持向量回归机(PDF)
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
期数:
2009年04
页码:
581-585
栏目:
出版日期:
2009-07-15

文章信息/Info

Title:
Thin wall tube method based sparse least squares support vector regression
作者:
赵永平; 孙健国;
南京航空航天大学能源与动力学院;
Author(s):
ZHAO Yong-ping; SUN Jian-guo
College of Energy and Power Engineering; Nanjing University of Aeronautics and Astronautics; Nanjing 210016; China
关键词:
支持向量机 最小二乘 稀疏性 剪枝算法 薄壁管法
Keywords:
support vector machines least squares approximations sparseness pruning algorithms thin wall tube method
分类号:
TP18
DOI:
-
文献标志码:
A
摘要:
针对最小二乘支持向量回归机的解存在缺乏稀疏性的缺陷,并结合支持向量分类机选择支持向量的特点,提出了薄壁管算法。通过样本的学习误差构造一个中空的有穷厚壁的管即薄壁管,把支持向量压缩到管壁当中,以克服传统的剪枝算法由于构造无穷厚壁管而无法抑制系统中奇异点的缺陷。与已有的拥有无穷厚壁的剪枝算法相比,薄壁管算法不仅能大大减少最小二乘支持向量回归机的支持向量数目和缩短预测时间,而且能成功抑制系统中存在的奇异点,提高最小二乘支持向量回归机的预测精确度。仿真实例验证了薄壁管法的有效性。
Abstract:
The solution of least squares support vector regression(LS-SVR) is lack of sparseness.To this end,after combing with the method of selecting support vectors in support vector classifier,the thin wall tube method(TWTM) is proposed.TWTM constructed a hollow tube with the finite wall through learning errors,which was different from the traditional pruning algorithms which select support vectors through infinite wall tubes so that outliers are not oppressed.Compared with the existing pruning algorithms,TWTM did...

参考文献/References:

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备注/Memo

备注/Memo:
国家自然科学基金(50576033)
更新日期/Last Update: 2010-05-25