Multiple Support Vector Domain Classifier

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Multiple Support Vector Domain Classifier

更新时间：2025-07-09

- Multiple Support Vector Domain Classifier
- Vol. 46, Issue 6, Pages: 87-91(2012)
- 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：
- DOI：
  CLC： TP301
- Online First：10 June 2012，
  
  Published：2012
- 稿件说明：
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Multiple Support Vector Domain Classifier[J]. 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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