CIESC Journal ›› 2010, Vol. 61 ›› Issue (2): 420-424.
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CAO Wei, ZHAO Yingkai, GAO Shiwei
Online:
Published:
曹巍,赵英凯,高世伟
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.
Key words: 支持向量机, 多类分类, 模糊核, 二叉树
支持向量机,
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。
关键词: 支持向量机, 多类分类, 模糊核, 二叉树
CAO Wei, ZHAO Yingkai, GAO Shiwei. Multi-class support vector machines based on fuzzy kernel cluster[J]. CIESC Journal, 2010, 61(2): 420-424.
曹巍, 赵英凯, 高世伟. 基于模糊核聚类的多类支持向量机 [J]. 化工学报, 2010, 61(2): 420-424.
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