计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (7): 185-189.

• 数据库与信息处理 • 上一篇    下一篇

支持向量机多类分类算法新研究

余 辉1,赵 晖1,2   

  1. 1.新疆大学 信息科学与工程学院,乌鲁木齐 830046
    2.北京科技大学 信息学院,北京 100083
  • 收稿日期:2007-06-18 修回日期:2007-09-10 出版日期:2008-03-01 发布日期:2008-03-01
  • 通讯作者: 余 辉

New research on multi-classification based on Support Vector Machines

YU Hui1,ZHAO Hui1,2   

  1. 1.College of Information Science and Engineering,Xingjinag University,Urumqi 830046,China
    2.College of Information Science and Technology,University of Science and Technology Beijing,Beijing 100083,China
  • Received:2007-06-18 Revised:2007-09-10 Online:2008-03-01 Published:2008-03-01
  • Contact: YU Hui

摘要: 支持向量机最初是针对两类分类问题提出的,如何将其推广至多类分类问题是当前SVM研究中的热点问题之一。主要针对支持向量机多类分类方法中的分解重构法进行了深入分析,详细讨论了影响分类器性能的两个关键因素:分解策略和组合策略,并通过实验验证了该观点。最后,通过实验对比了包括M-ary 支持向量机和模糊支持向量机的SVM多类分类方法。

Abstract: Support Vector Machines(SVMs) is originally designed for binary classification.How to extend it for multi-category classification is one of hot research issues.This paper emphasizes on analyzing decomposing and reconstitution methodology of SVM multi-classification algorithms,and discusses two key factors which affect performances of categories in detail:decomposing strategy and composing strategy.The paper does experiments to validate authors’ opinions and then comparesall kinds of SVM multi-category classification,including M-ary SVMs and fuzzy SVMs.