Multi-class support vector machines based on fuzzy kernel cluster

CAO Wei; ZHAO Yingkai; GAO Shiwei

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Multi-class support vector machines based on fuzzy kernel cluster

更新时间：2025-12-10

- Multi-class support vector machines based on fuzzy kernel cluster
- CIESC Journal Issue 2, Pages: 420-424(2010)
- 作者机构：
  
  1. 中国石油兰州石化自动化研究院
  2. 南京工业大学自动化学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：05 February 2010，
  
  Published：05 February 2010
- 稿件说明：
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CAO Wei, ZHAO Yingkai, GAO Shiwei. Multi-class support vector machines based on fuzzy kernel cluster[J]. CIESC Journal, 2010, (2): 420-424.
DOI：

CAO Wei, ZHAO Yingkai, GAO Shiwei. Multi-class support vector machines based on fuzzy kernel cluster[J]. CIESC Journal, 2010, (2): 420-424. DOI：

摘要

传统的支持向量机是基于两类问题提出的，如何将其有效地推广至多类问题仍是一个值得研究的问题。本文在比较常用的几种多类支持向量机分类算法基础上，提出了一种基于模糊核聚类的多类支持向量机分类方法。支持向量机的分类精度和分类速度取决于树结构，新方法利用模糊核聚类生成模糊类，并结合基于二叉树的多类支持向量机分类算法实现多类分类。实验结果表明，该方法是一种效率更高、分类更准确的多类支持向量机分类算法。

Abstract

Traditional support vector machines (SVM) is originally designed for binary classification.How to effectively extend it to multi-class classification is worthy to research. This paper compared some common support vector machines for multi-class classification problems

and proposed a multi-class support vector machine based on fuzzy kernel clustering algorithm.The classification accuracy and classification speed of support vector machine depended on the tree structure.This multi-class support vector machine used fuzzy kernel clustering algorithm to generated fuzzy class

and combined with the multi-class SVM based on binary tree classification algorithm for multi-class classification.Experimental results showed that the proposed method was more effective and accurate.

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