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计算机工程 ›› 2008, Vol. 34 ›› Issue (13): 172-173,. doi: 10.3969/j.issn.1000-3428.2008.13.062

• 人工智能及识别技术 • 上一篇    下一篇

光滑支持向量机的原理和进展

熊金志,胡金莲,袁华强   

  1. (东莞理工学院软件学院,东莞 523808)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-07-05 发布日期:2008-07-05

Principles and Advances of Smooth Support Vector Machine

XIONG Jin-zhi, HU Jin-lian, YUAN Hua-qiang   

  1. (Software College, Dongguan University of Technology, Dongguan 523808)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-07-05 Published:2008-07-05

摘要: 支持向量机是数据挖掘的新方法,由于其显著的优点,因此得到了广泛的应用。光滑支持向量机是标准支持向量机的一种改进形式,其应用已显示出了优越性。该文论述光滑支持向量机(SSVM)的基本原理、SSVM 模型、多项式光滑的支持向量机模型以及一类新的光滑函数。讨论光滑支持向量机目前尚未解决的一些关键问题,并展望了今后的发展趋势,为进一步研究光滑支持向量机理论提供了基本思路。

关键词: 模式识别, 分类, 支持向量机, 光滑

Abstract: Support Vector Machine(SVM) is a new technique for data mining. It has wide applications in various fields and is a research hot pot of the machine learning field. Due to its differentiability, smooth SVM, a reformulation of standard SVM, has shown advantages in many aspects. This paper introduces the basic principle of smooth SVM, SSVM model, PSSVM model and a new class of smoothing functions, it focuses on the open and key problems in this field. It discusses some promising directions of smooth SVM research for future work.

Key words: pattern recognition, classification, Support Vector Machine(SVM), smooth

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