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统计学习理论和支持向量机 被引量：14

Statistical Learning Theory and Support Vector Machine
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摘要 介绍了统计学习理论和支持向量机的概貌,以及目前支持向量机方法研究的现状· General picture and development in the domain of SLT and SVM are reviewed; actuality of investigation on SVM is also introduced.
作者 宇缨 李清华
机构地区 沈阳大学机械工程学院
出处 《沈阳大学学报》 CAS 2005年第4期42-47,共6页
关键词 统计学习理论 结构风险最小化 支持向量机 分类 核 statistical learning theory （SLT） structural risk minimization support vector machine（SVM） classification kernel
分类号 C81 [社会学—统计学]
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参考文献18

  • 1[1]Vapnik V and Lerner A. A pattem recognition using generalized portrait method [J]. Automation and Remote Control, 1963,24:.
  • 2[3]Corinna Cortes, Vladimir Vapnik. Support-Vector Networks [J]. Machine Learning, 1995,20(3) :273-297.
  • 3[4]Vapnik V. Statistical leoing theory[M]. New York:John Wiley & Sons, 1998
  • 4[5]Scholkopf B, Smola A J. Learning with kernels [M].Cambridge,MA: MIT Press, 2002.
  • 5[6]Scholkopf B, Plat J C, Shawe-Taylor J, et al. Estimating the support of a high-dimensional distribution [J]. Neural Computation, 2001,13(7): 1443-1471.
  • 6[7]Joachims T. Transductive inference for text classification using support vectoor machine[A]. In prooceedings of the Sixteenih International Conference on Machine Learning [C]. Morgan Kaufmann, 1999:148-156.
  • 7陈毅松,汪国平,董士海.基于支持向量机的渐进直推式分类学习算法[J].软件学报,2003,14(3):451-460. 被引量：89
  • 8[10]Yiqiang Zhan, Dinggang Shen. Design efficient support vector machine for fast classification [J]. Pattern Recognition, 2005,38:157-161.
  • 9[11]Zhang X. Using class-center vectors to build support vector machines[A]. InPrroceedingss of NNSP'99[C], 1999.
  • 10[12]Mao K Z. Feature subset selection for support vector machines through discriminative function pruning analysis [J]. Ieee Transaction on Systems, Man, and Ctbernetics Part B: Cybernetics, 2004,34(1):60-67.

二级参考文献17

  • 1[1]Vapnik V. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
  • 2[2]Stitson MO, Weston JAE, Gammerman A, Vovk V, Vapnik V. Theory of support vector machines. Technical Report, CSD-TR-96-17, Computational Intelligence Group, Royal Holloway: University of London, 1996.
  • 3[3]Cortes C, Vapnik V. Support vector networks. Machine Learning, 1995,20:273～297.
  • 4[4]Vapnik V. Statistical Learning Theory. John Wiley and Sons, 1998.
  • 5[5]Gammerman A, Vapnik V, Vowk V. Learning by transduction. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Wisconsin, 1998. 148～156.
  • 6[6]Joachims T. Transductive inference for text classification using support vector machines. In: Proceedings of the 16th International Conference on Machine Learning (ICML). San Francisco: Morgan Kaufmann Publishers, 1999. 200～209.
  • 7[7]Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. In: Haussler D, ed. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory. Pittsburgh, PA: ACM Press, 1992. 144～152.
  • 8[8]Burges CJC. Simplified support vector decision rules. In: Saitta L, ed. Proceedings of the 13th International Conference on Machine Learning. San Mateo, CA: Morgan Kaufmann Publishers, 1996. 71～77.
  • 9[9]Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines. In: Proceedings of the IEEE NNSP'97. Amelia Island, FL, 1997. 276～285.
  • 10[10]Joachims T. Making large-scale SVM learning practical. In: Scholkopf, Burges C, Smola A, eds. Advances in Kernel Methods--Support Vector Learning B. MIT Press, 1999.

共引文献88

同被引文献166

引证文献14

二级引证文献140

沈阳大学学报
2005年 第4期

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