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基于特征空间聚类的二叉树支持向量机分类算法
引用本文:李志永,陈立潮,张英俊.基于特征空间聚类的二叉树支持向量机分类算法[J].计算机与数字工程,2010,38(6):32-34.
作者姓名:李志永  陈立潮  张英俊
作者单位:太原科技大学计算机科学与技术学院,太原,030024
摘    要:采用数据挖掘中聚类分析的类距离定义,在高维特征空间中,计算各类别间的最短距离,以最短距离作为该类与其他类的距离,提出了一种基于聚类-二叉树支持向量机分类算法。该算法能够简化计算,同时通过类距离比较实现了对类距离最大者的优先分离,实验结果表明该算法具有一定的优越性。

关 键 词:支持向量机  二叉树  聚类  多分类

Feature-based Spatial Clustering of the Binary Support Vector Machine Classification Algorithm
Li Zhiyong,Chen Lichao,Zhang Yingjun.Feature-based Spatial Clustering of the Binary Support Vector Machine Classification Algorithm[J].Computer and Digital Engineering,2010,38(6):32-34.
Authors:Li Zhiyong  Chen Lichao  Zhang Yingjun
Affiliation:Li Zhiyong Chen Lichao Zhang Yingjun(Institute of Computer Science & Technology,Taiyuan University of Science and Technology,Taiyuan 030024)
Abstract:Cluster analysis using data mining from the definition of the class,in hingh-dimensional feature space,calculating the shortest distance between the various categories,the shortest distance as the distence between the class and others.Proposed a clustering-based-binary tree support vector machine classification algorithm.The algorithm can simplify the calculation,experimental results show that the algorithm has some advantages.
Keywords:support vector machine  binary tree  clustering  multi-classification
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