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计算机工程 ›› 2013, Vol. 39 ›› Issue (1): 183-186. doi: 10.3969/j.issn.1000-3428.2013.01.039

• 人工智能及识别技术 • 上一篇    下一篇

模糊最小包含球支持向量机

刘建华,龚松杰   

  1. (浙江工商职业技术学院工学院,浙江 宁波 315012)
  • 收稿日期:2011-09-19 修回日期:2011-12-06 出版日期:2013-01-15 发布日期:2013-01-13
  • 作者简介:刘建华(1979-),男,讲师、硕士,主研方向:模式分类,人工智能;龚松杰,硕士研究生
  • 基金资助:
    宁波市自然科学基金资助项目(2009A610080);浙江省教育厅科研基金资助项目“支持服务质量语义Web服务发现关键技术研究”(Y201224057)

Fuzzy Minimal Enclosing Ball Support Vector Machine

LIU Jian-hua, GONG Song-jie   

  1. (Institute of Polytechnic, Zhejiang Business Technology Institute, Ningbo 315012, China)
  • Received:2011-09-19 Revised:2011-12-06 Online:2013-01-15 Published:2013-01-13

摘要: 为提高支持向量机的模式分类性能,综合模糊支持向量机和球形支持向量机等方法,提出一种模糊最小包含球(FMEB)支持向量机,对于模式分类问题,通过引入模糊隶属度,寻找2个分别包含二类模式的同心最小包含球,使类间间隔最大化,同时二类模式类内分布最小化,从而增强泛化性和鲁棒性。实验结果证明FMEB的模式分类性能优于其他方法。

关键词: 泛化, 支持向量机, 模糊最小包含球, 超球分类机, 核函数

Abstract: In order to improve the classification performance of hypersphere Support Vector Machine(SVM), this paper proposes Fuzzy Minimum Enclosing Ball(FMEB) SVM by integrating several state-of-art classification methods such as Fuzzy Support Vector Machine(FSVM) and Hypersphere Support Vector Machine(HSVM). For pattern classification problem, the basic idea of FMEB is to find two optimal minimum enclosing hyperspheres by introducing fuzzy membership, so that each binary class is enclosed by them respectively, and the margin between one class pattern and the enclosing hypersphere is maximized, thus improving the generalization performance and robustness of hypersphere SVM. Experimental results prove that FMEB is more effective than other methods.

Key words: generalization, Support Vector Machine(SVM), Fuzzy Minimum Enclosing Ball(FMEB), hypersphere classifier, kernel function

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