Multi-Class Support Vector Domain Description for Pattern Recognition Based on a Measure of Expansibility

ZHU Xiao-kai; YANG De-gui

您当前的位置：

首页 >

文章列表页 >

Multi-Class Support Vector Domain Description for Pattern Recognition Based on a Measure of Expansibility

更新时间：2025-07-16

- Multi-Class Support Vector Domain Description for Pattern Recognition Based on a Measure of Expansibility
- Acta Electronica Sinica Vol. 37, Issue 3, Pages: 464-469(2009)
- 作者机构：
  
  国防科学技术大学电子科学与工程学院空间电子信息技术研究所,湖南,长沙,410073
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.4
- Published：2009
- 稿件说明：
移动端阅览
ZHU Xiao-kai, YANG De-gui. Multi-Class Support Vector Domain Description for Pattern Recognition Based on a Measure of Expansibility[J]. Acta Electronica Sinica, 2009, 37(3): 464-469.
DOI：

ZHU Xiao-kai, YANG De-gui. Multi-Class Support Vector Domain Description for Pattern Recognition Based on a Measure of Expansibility[J]. Acta Electronica Sinica, 2009, 37(3): 464-469. DOI：

摘要

经典的基于距离测度的SVDD(Support Vector Domain Description)方法在解决两类(多类)识别问题时具有误判率较高、识别率低于普通二类SVC分类器等缺点.本文在分析其原因的基础上

提出了一种更能反映样本与类别本质关系的推广能力测度

并由此提出了具有多层结构的多类SVDD模式识别方法.对实测雷达一维距离像数据的测试表明

该方法在保留了经典SVDD识别器算法复杂程度低、扩充性强、对训练样本数据规模上要求低等优点的同时

有效地降低了误判率

识别率已接近甚至达到二类SVC的水平.

Abstract

Classic SVDD classifiers

which use distance measures

have lower recognition rate than normal two-class SVM classifiers.After analyzing the causes

a new measure is proposed

which can represent the more essential relationship between samples and categories.And then

a multi-level SVDD is proposed.The experiment on real one-dimensional range profiles data shows that

the multi-level SVDD reserves the lower complexity

higher expansibility and fewer requirements to sample size

ERs are reduced effectively

CR is increased even to the level of two-class SVM classifiers.

关键词

Keywords

references

Views

980

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Anti-Noise Nonparallel Support Vector Machine with Margin Distribution

High Correct Recognition Rate Classifier Design with Appropriate Rejection Mechanism

A Survey on Visible-infrared Cross-modality Person Re-identification

A Theoretical Result on the Generalized Optimal Set of Statistically Uncorrelated Discriminant Vectors

A New Approach for Face Recognition Based on Singular Value Features and Neural Networks

Related Author

LIU Li-ming

LI Ping

CHU Mao-xiang

CAI Hong-bin

YANG Guo-wei

WAN Ming-hua

LAI Zhi-hui

ZHANG Fan-long

Related Institution

School of Electronic and Information Engineering， University of Science and Technology Liaoning

School of Electronic Information, Qingdao University

School of Information Engineering, Nanjing Audit University

School of Computer Science and Software Engineering, Shenzhen University

Hong Kong Polytechnic University, Kowloon

⁰