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概论核方法及核参数的选择 被引量：1

A Summary of Kernel Methods and the Selection of the Kernel Parameters
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摘要 本文介绍支持向量机分类非线性数据集的优越性,讨论了基于核的方法,并对核的方法的实质进行了论述。通过matlab制作的图像可知:核方法的参数的选择对于基于核的分类法具有重要作用。对现有的选择核参数的方式效果进行了归纳与比较,从而分析得出了各种选择参数方式的优缺点。 In this paper, we illustrated the superiority of SVM in classifying the non - linear datasets, discussed the kernel - based method and debated its essence. The figures made by Matalab software showed that, the selection of parameters was crucial for the kernel - based method. Various methods in the selection of the kernel parameters were included, which was associated with kernel - based methods, and their efficacies were compared as well. Further, their advantages and disadvantages were analyzed.
作者 邱潇钰 张化祥
机构地区 山东师范大学信息科学与工程学院
出处 《信息技术与信息化》 2007年第6期63-65,共3页 Information Technology and Informatization
基金 山东省科技攻关计划(2005GG4210002) 山东省青年科学家科研奖励基金(2006BS01020)
关键词 支持向量机 非线性数据集 基于核的方法 核参数 Svm The non - linear Datasets The kernel - method The kernel parameters
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献14

  • 1LIN C-F,WANG S-D.Fuzzy support vector machines[J].IEEE Trans on Neural Networks,2002,13(2):464-471.
  • 2Frauke Friedrichs,Christian Igel.Evolutionary tuning of multiple SVM parameters[J],Neurocomputing 64 (2005) 107-117.
  • 3David V Sanchez A.Advanced support vector machines and kernel methods[J].Neurocomputing,2003,55:5-20.
  • 4LASKOV P.Feasible direction decomposition algorithms for training support vector machines[J].Machine Learning,2002,46(1):315-349.
  • 5O Chapelle,V Vapnik.Choosing multiple parameters for Support Vector Machines[J].Machine Learning,2002,46:131-159.
  • 6Vapnik V N.The Nature of Statistical Learning Theory[M].NY:Springer,1995.
  • 7Colin Campbell.Kernel methods:A survey of current techniques[J].Elsevier,Neurocomputing 48 (2002) 63-84.
  • 8Vlad Popovicia,,Samy Bengiob,Jean-Philippe Thirana," Kernel matching pursuit for large datasets[J].Pattern Recognition 38 (2005) 2385-2390
  • 9Fernando Pérez-Cruz and Olivier Bousquet.Kernel Methods and Their Potential Use in Signal Processing[J].IEEE SIGNAL PROCESSING MAGAZINE MAY 2004 57 1053-5888.
  • 10B.E.Boser,I.M.Guyon,and V.N.Vapnik,A training algorithm for optimal margin classifier[J],in Proc.5th Annu.ACM Workshop Compute.Learning Theory,D.Haussier,Ed,1992,pp.144-152.

二级参考文献10

  • 1David V,Sanchez A.Advanced support vector machines and kernel methods[J].Neurocomputing,2003,55:5-20.
  • 2Wang W J,Xu Z B,Lu W Z,et al.Determination of the spread parameter in the Gaussian kernel for classification and regression[J].Neurocomputing,2003,55(3-4):643-663.
  • 3Duan K B,Keethi S,Poo A N.Evaluation of simple performance measure for tuning SVM hyperparameters[J].Neurocomputing,2003,51:41-59.
  • 4Cherkassky V,Ma Y Q.Practical selection of SVM parameters and noise estimation for SVM regression[J].Neural Networks,2004,17:113-126.
  • 5Anguita D,Ridella S,Rivieccio F,et al.Hyperparameter design criteria for support vector classifiers[J].Neurocomputing,2003,55:109-134.
  • 6Golda C,Sollichb P.Model selection for support vector machine classification[J].Neurocomputing,2003,55:221-249.
  • 7邓乃杨,田英杰.数据挖掘中的新方法-支撑向量机[M].北京:科学出版社,2004.
  • 8Vapnik V.The nature of statistical learning theory[M].Springer Verlag Press,1995.
  • 9Cristinini N,Shawe-taylor J.An introduction to support vector Machine and other kernel-based learning machine[M].Cambridge Press,2000.
  • 10陶卿,孙德敏,范劲松,方廷健.基于闭凸包收缩的最大边缘线性分类器[J].软件学报,2002,13(3):404-409. 被引量：17

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信息技术与信息化
2007年 第6期

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