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author:

孙昌儿 (孙昌儿.) [1] | 刘秉瀚 (刘秉瀚.) [2] (Scholars:刘秉瀚)

Abstract:

SVM在小训练样本,高维情况下,具有很好的泛化性能.但它不适用于多类分类.本文分析基本的SVM和多类SVM分类器,重点讨论了SVM决策树,提出了一种结点分类器类集合划分方案来构造SVM决策树.实验结果表明,这种方法构造的SVM决策树分类器分类性能较好.

Keyword:

SVM决策树 分类器 支持向量机 训练样本

Community:

  • [ 1 ] [孙昌儿]福州大学
  • [ 2 ] [刘秉瀚]福州大学

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Source :

Year: 2006

Page: 366-369

Language: Chinese

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count: -1

Chinese Cited Count:

30 Days PV: 1

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