支持向量域多分类器

吴德; 刘三阳

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支持向量域多分类器

更新时间：2025-07-09

- 支持向量域多分类器
- Multiple Support Vector Domain Classifier
- 西安交通大学学报 2012年46卷第6期页码：87-91
- 作者机构：
  
  西安电子科技大学计算机学院,西安,710071
- 作者简介：
- 基金信息：
  
  : 2011-10-14. 作者简介: 吴德(1979-),男,博士生;刘三阳(通信作者),男,教授,博士生导师. 基金项目: 国家自然科学基金资助项目(60974082);陕西省教育厅自然科学基金资助项目(2010JK773);西安石油大学博士专项科研基金资助项目(Z10027)
- DOI：
  中图分类号： TP301
- 网络首发：2012-06-10，
  
  纸质出版：2012
- 稿件说明：
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吴德, 刘三阳. 支持向量域多分类器[J]. 西安交通大学学报, 2012,46(6):87-91.

Multiple Support Vector Domain Classifier[J]. 2012, 46(6): 87-91.
吴德, 刘三阳. 支持向量域多分类器[J]. 西安交通大学学报, 2012,46(6):87-91. DOI：

Multiple Support Vector Domain Classifier[J]. 2012, 46(6): 87-91. DOI：

摘要

为解决多分类支持向量机计算量大、训练时间长的问题

构造了支持向量域多分类器(MSVDC).在训练阶段

运用支持向量域描述求得各类样本的最小包围超球

进而将数据空间划分为不同区域; 在测试阶段

计算待识别样本与最小包围超球球心的距离

并判断其空间位置; 对超球重叠以及超球外区域的样本

定义一种相对类距离

判断样本归属该值较小的类.MSVDC避免了重复利用训练样本

降低了内存占用并提高了计算效率.数值实验结果表明:MSVDC具有好的鲁棒性

分类精度可高达98.89%

分别比一对多和一对一算法高4.51%和1.24%

训练时间分别为一对多和一对一算法的18.06%和55.41%.

Abstract

To solve the difficulties in multi SVM

such as huge computation and long training time

a multiple support vector domain classifier(MSVDC)is proposed. In the training process

the support vector domain description(SVDD)is employed to obtain the minimal enclosing ball(MEB)of each class

and then the data space is divided into different regions. In the test phase

the distances from the test sample to the MEB centers are evaluated

and the position of the test sample is determined. For samples in the overlapped and outside regions of the MEB

a relative class distance is defined

and is erected in the class with the smallest value. MSVDC avoids the repeated usage of training data

and reduces the memory and enhance the efficiency. Numerical experiments show that MSVDC is endowed with better robustness. The classification accuracy gets to 98.89%

4.51% and 1.24% higher than “one-against-all” and “one-against-one”

and the training time is only spent for 18.06% and 55.41% of “one-against-all” and “one-against-one”

respectively.

关键词

Keywords

references

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陆从德,张太镒,胡金燕.基于乘性规则的支持向量域分类器[J].计算机学报,2004,27(5):690-694.

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梁锦锦,刘三阳,吴德.空间支持向量域分类器[J].西安电子科技大学学报,2008,35(6):1080-1083.

LIANG Jinjin,LIU Sanyang,WU De. Space support vector domain classifier [J]. Journal of Xidian University,2008,35(6):1080-1083.

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