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基于特征空间中样本选取与分离的SVM简化方法 被引量：4

Simplified SVM method based on sample selection and separation in eigenspace
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摘要 在特征空间中将样本做分离,将高维的二次规划问题化成数个低维的二次规划问题的组合,大大降低了训练SVM的运算量,而又基本不损害SVM的性能,这为SVM在模式分类中的实时应用创造了条件。 In the eigenspace the samples are divided to transform the quadratic program in the high dimensions to the one in the low. The method keep the properties of SVM and offer the real-time application conditions in the mode classification.
作者 王勇
机构地区 长春工业大学基础科学学院
出处 《长春工业大学学报》 CAS 2008年第5期486-491,共6页 Journal of Changchun University of Technology
基金 国家自然科学基金资助项目(10471055)
关键词 支持向量机 特征空间 分类 二次规划问题 SVM eigenspaee classification quadratic programe.
分类号 O24 [理学—计算数学]
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参考文献11

  • 1Vapnic V. The nature of statistical learning thedry [C]. [S. l.]: Springer-Verlag,1995.
  • 2Cortes C, Vapnic V. Support vector networks[J]. Machine Learning, 1995,20 : 1-25.
  • 3Pontil, Verri. Proporties of support vector machines[J]. Neural Computation, 1998,10: 955-974.
  • 4Vapnic V. Estimation of dependences based on em pirical data[C]. [S. l.]:Springer-Verlag, 1982.
  • 5Osuna, Girosi. Reducing the run-time complexity of support vector machines[C]. [S. l.]: To appear in ICPR' 98,1998.
  • 6Amari Wu. Improve support vector machines classifiers by modifying kernel/unctions[J]. Neural Network, 1999,12 : 783-789.
  • 7Barzilay, Brailovsky. On domain knowledge and feature selection using a support vector machine [J]. Pattern Recognition Letters, 1999, 20: 475- 484.
  • 8Barzilay, Brailovsky, Shahave. On global, local, mixed and neighborhood kernels for support vector machines [J]. Pattern Recognition Letters, 1999, 20:1 183-1 190.
  • 9Platt J C. Fast training of support vector machines using sequential minimal optimization[C]. In: Advances in Kernel Methods: Support Vector Machines, B. Scholkopf, C. Burges, and A. Smola, Eds. Combridge, MA : MIT Press, 1998.
  • 10Keerthi S, Shevsde S, Bhattacharyya C, et al. A fast iterative nearest point algorithm for support vector machine classifier design[J]. IEEE Trans. Neural Network,2000, (11): 124-136.

同被引文献37

  • 1李昆仑,黄厚宽,田盛丰,刘振鹏,刘志强.模糊多类支持向量机及其在入侵检测中的应用[J].计算机学报,2005,28(2):274-280. 被引量：49
  • 2李健,范万春,何驰.基于多分类支持向量机的网络入侵检测技术[J].计算机应用,2005,25(7):1551-1553. 被引量：7
  • 3田新梅,吴秀清,刘莉.大样本情况下的一种新的SVM迭代算法[J].计算机工程,2007,33(8):205-207. 被引量：4
  • 4Vapnik V.The nature of statistical learning theory[M].New York: Spring-Verlag, 1995.
  • 5Cortes C, Vapnik V. Suport vector networks [J]. Machine Learning, 1995,20:273-297.
  • 6Osuna E,Freund R,Girosi F.An improvend trainging algorithm for support vector machines[C].Nerual Networks for Signal Processing Ⅶ-Proceedings of the 1997 IEEE Workshop, 1997: 276-285.
  • 7Platt J.Fast traing of support vector machines using sequential minimal optimization[C].Advances in Kernel Methods-Support Vector Learing.MIT Press, 1999:185-208.
  • 8Lee Y, Mangasarian O L.RSVM:Reduced support vector machines[C].San Jose,CA:Proc of the SIAM International Conference on Data Mining,2001.
  • 9Almeida M B,Braga A P, Braga J P.SVM-km:Speeding SVMs learning with a priori cluster selection and K-means[C].Proc of the 6th Brazilian Symposium on Neural Networks, 2000:162-167.
  • 10Shin H,Cho S.Neighborhood property based pattern selection for support vector machines [J]. Neural Computation, 2007,19 (3): 816-855.

引证文献4

二级引证文献10

长春工业大学学报
2008年 第5期

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