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支持向量分类方法理论基础的改进(英文) 被引量：2

An Improvement to the Theoretical Foundation of Support Vector Classification
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摘要 支持向量机是通过求解对偶问题来解决原始问题的.针对线性决策函数f(x)=(w·x)+b,我们指出了其原有的逻辑系统中的错误,并通过严格的证明,对其理论基础作了改进.而且,对于阈值b,我们给出了一个新的简洁计算公式. The basic Support Vector Machine for Classification solves the primal problem by solving the dual problem. Considering the linear decision function f(x)=(w·x)+b, an essential drawback in its logic system is pointed out and a strict theoretical foundation is established. Furthermore, for computing the threshold b, a new compact formula is proposed first time.
作者 张春华 田英杰 张跃峰
机构地区 中国农业大学经济管理学院
出处 《运筹学学报》 CSCD 北大核心 2004年第2期66-71,共6页 Operations Research Transactions
基金 This work is supported by the National Natural Science Foundation of China (No. 10371131).
关键词 线性决策函数 逻辑系统 阈值 运筹学 支持向量分类 Wolfe对偶 KKT条件 OR, Support Vector Classification, Wolfe Dual, KKT conditions.
分类号 O221.1 [理学—运筹学与控制论] O174 [理学—基础数学]
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参考文献10

  • 1B.Schōlkopf and A.J.Smola. Learning with Kernels-Support Vector Machines, Regularization,Optimization, and Beyond. The MIT Press, 2002.
  • 2N.Cristianini and J.Shawe-Taylor. An introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.
  • 3C.J.C.Burges and D.J.Crisp. Uniqueness of the SVM Solution. In Proceedings of the Twelfth Conference on Neural Information Processing Systems. S. A. Solla, T. K. Leen, and K.-R. Mller(Eds.), MIT Press, 1999.
  • 4C.J.C.Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data mining and Knowledge Discovery, 1998, 2(2):121-167.
  • 5S.G.Nash and A.Sofer. Linear and Nonlinear Programming. George Mason University, 1996.
  • 6B.Scholkopf and A.J.Smola. Learning with Kernels-Support Vector Machines, Regularization,Optimization, and Beyond. The MIT Press, 2002.
  • 7N.Cristianini and J.Shawe-Taylor. An introduction to Support Vector Machines aud Other Kernel-based Learning Methods. Cambridge University Press, 2000.
  • 8C.J.C.Burges and D.J.Crisp. Uniqueness of the SVM Solution. In Proceedings of the Twelfth Conference on Neural Information Processing Systems. S. A. Solla, T. K. Leen, and K.-R. Mller (Eds.), MIT Press, 1999.
  • 9C.J.C.Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data mining and Knowledge Discovery, 1998, 2(2):121-167.
  • 10S.G.Nash and A.Sofer. Linear and Nonlinear Programming. George Mason University, 1996.

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运筹学学报
2004年 第2期

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