A Weighted Solution Path Algorithm for Support Vector Regression

毛文涛; 闫桂荣; 董龙雷; 张刚

您当前的位置：

首页 >

文章列表页 >

A Weighted Solution Path Algorithm for Support Vector Regression

更新时间：2025-12-08

- A Weighted Solution Path Algorithm for Support Vector Regression
- Vol. 42, Issue 10, Pages: 1226-1229+1274(2008)
- 作者机构：
  
  西安交通大学强度与振动教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP181
- Online First：10 October 2008，
  
  Published：2008
- 稿件说明：
移动端阅览
毛文涛, 闫桂荣, 董龙雷, et al. A Weighted Solution Path Algorithm for Support Vector Regression[J]. 2008, 42(10): 1226-1229+1274.
DOI：

毛文涛, 闫桂荣, 董龙雷, et al. A Weighted Solution Path Algorithm for Support Vector Regression[J]. 2008, 42(10): 1226-1229+1274. DOI：

摘要

针对不同训练样本重要性的差异对模型推广能力的影响

提出了对各个样本的误差惩罚参数赋予不同权重的加权支持向量机求解路径算法.根据样本重要性的不同

利用分段线性插值得到加权系数

并通过加权系数调整求解路径

从而改变不同样本在回归模型中的作用.采用支持向量机加权求解路径算法对圆柱壳结构在不同边界条件下的时、频域响应数据进行预测

训练样本的重要性通过与测试样本的欧式距离来表达

结果显示所提算法可减小位移响应在多个评价指标下的预测误差

提高支持向量回归机的推广能力.该方法同样适用于其他求解路径算法

如λ-路径算法和ν-支持向量回归路径算法.

Abstract

Considering the existence of significant difference among the training samples

a weighted solution path algorithm for support vector regression is proposed in which the error penalty parameter on each training sample is weighted differently. The weighting coefficients are obtained by using piecewise linear interpolation based on the difference of samples' importance. Then the solution path is adjusted by the resulting weighting coefficients

such that the effects of different samples in regression model are changed correspondingly. The experiments on the prediction of response data of cylinder structure under two different boundary conditions in time frequency domain

where the importance of each training sample is characterized by the geometrical distance to the test sample

show that the weighted solution path algorithm for support vector regression can reduce prediction errors of displacement response on different evaluation indexes

and that the generalization of regression model is improved. It is noted that the main idea of the proposed algorithm can also be applied to other solution path algorithms

such as λ-path and ν-SVR path algorithms.

关键词

Keywords

references

ROSSET S, ZHU J. Piecewise linear regularized solution paths[J]. Annals of Statistics, 2007, 35(3): 1012-1030.

杜树新,吴铁军. 回归型加权支持向量机方法及其应用 [J]. 浙江大学学报(工学版),2004, 38(3),302-306.

DU Shuxin, WU Tiejun. Weighted support vector machines for regression and its application [J]. Journal of Zhejiang University(Engineering Science), 2004, 38(3): 302-306.

TOY F E H, CAO L J. Modified support vector machines in financial time series forecasting [J]. Neurocomputing, 2002,48(1/4): 847-861.

ZHU J, ROSSET S, HASTIE T, et al. 1-norm support vector machines [C]∥Advances in Neural Information Processing Systems. Cambridge,USA: MIT Press, 2004: 49-56.

HASTIE T, ROSSET S, TIBSHIRANI R, et al. The entire regularization path for the support vector machine[J]. Journal of Machine Learning Research,2004, 5:1391-1415.

GUNTER L, ZHU J. Efficient computation and model selection for the support vector regression [J]. Neural Computation, 2007, 19(6): 1633-1655.

WANG G, YEUNG D Y, LOCHOVSKY F H. Two dimensional solution path for support vector regression[C]∥Proceedings of the 23rd International Conference on Machine Learning. New York, USA: ACM Press, 2006: 993-1000.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

On-Line Support Vector Regression by Multirate Sampling with Application

Classification Performance of Support Vector Machine with ε-Insensitive Loss Function

Improvement and Optimization of Multi-Blade Centrifugal Fan Impeller Based on Variable Chord Length Blade Design

Integrated and Joint Design Optimization of Centrifugal Compressor Impeller and Casing Treatment at Multiple Working Points

Bi-Level Multi-Obj ective Optimization of a Wind-Solar Coupled Hydrogen Production System Based on the Synergy of Hybrid Electrolyzer Arrays

Related Author

李大海 1

李天石 1

李宗斌 2

杨俊燕

张优云

朱永生

LEI Jian

QIN Guoliang

Related Institution

School of Mechanical Engineering, Xi'an Jiaotong University

Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University

(School of Energy and Power Engineering, Xi'an Jiaotong University,,)

⁰