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

杨俊燕; 张优云; 朱永生

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

更新时间：2025-12-08

- Classification Performance of Support Vector Machine with ε-Insensitive Loss Function
- Vol. 41, Issue 11, Pages: 1315-1320(2007)
- 作者机构：
  
  西安交通大学现代设计和转子轴承系统教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP183
- Online First：10 November 2007，
  
  Published：2007
- 稿件说明：
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杨俊燕, 张优云, 朱永生. Classification Performance of Support Vector Machine with ε-Insensitive Loss Function[J]. 2007, 41(11): 1315-1320.
DOI：

杨俊燕, 张优云, 朱永生. Classification Performance of Support Vector Machine with ε-Insensitive Loss Function[J]. 2007, 41(11): 1315-1320. DOI：

摘要

将原先用于支持向量回归的ε不敏感损失函数引入到支持向量分类中

提出ε不敏感损失函数支持向量分类算法(ε-SVC).同标准支持向量分类方法(C-SVC)和最小二乘支持向量分类方法(LS-SVC)相比较

试验结果表明:当赋予参数ε一个足够大的接近于1的值时

ε-SVC的分类正确率略低于C-SVC和LS-SVC

但是ε-SVC的训练、测试和参数选择的速度要高于C-SVC和LS-SVC.特别是对于大规模数据集

这种优势将更加明显.另外

通过精确选择参数ε的值

ε-SVC能够获得比C-SVC和LS-SVC更高的分类正确率

但是训练、测试和参数选择的速度却随着ε的减小而降低.

Abstract

The ε-insensitive loss function generally employed in support vector regression is introduced into support vector classification

and the support vector classification with ε-insensitive loss function(ε-SVC)is proposed. Compared with the standard support vector classification method(C-SVC)and the least square support vector classification method(LS-SVC)

the experimental result indicates that the classification accuracy ratio of ε-SVC is slightly lower than that of C-SVC and LS-SVC when ε sufficiently approaches to 1

but the training

testing and parameter selecting rates of ε-SVC are superior to that of C-SVC and LS-SVC

especially for large scale problem. Through accurate search of the parameter ε

the ε-SVC is endowed with higher classification accuracy than C-SVC and LS-SVC

however

the training

testing and parameter selecting rates decrease with smaller ε.

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references

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