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计算SVM判别函数值的方法 被引量：1

New Method of Computing SVM Discriminant Value
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摘要 当支撑矢量或待判别的样本很多时 ,支撑矢量机算法对判别函数值的直接计算会影响整个SVM算法的速度 国外对于SVM的训练算法研究很深入 ,但判别函数值的算法研究很少 文中将从减少判别值计算的复杂性入手 ,提出矢量替换法 (主要针对线性SVM )、正交矢量法 (主要针对非线性SVM) When the number of support vectors or samples required to distinguish is large, its direct computation would take much time In order to reduce the complexity of discriminant value computation, vector replacement method mainly for linear SVM and orthogonal vectors method mainly for nonlinear SVM are brought forward
作者 王贵新 汪同庆 余静 宛西原 刘建胜 居琰
机构地区 重庆后勤工程学院基础部 重庆大学光电工程学院 重庆港口管理局建设发展处
出处 《计算机辅助设计与图形学学报》 EI CSCD 北大核心 2003年第6期720-723,729,共5页 Journal of Computer-Aided Design & Computer Graphics
关键词 支撑矢量机 SVM 函数值判别 矢量替换法 正交矢量法 support vector machine(SVM) algorithm support vector orthogonal vectors
分类号 TP391.41 [自动化与计算机技术—计算机应用技术] TP181 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献9

  • 1Vapnik V N. The Nature of Statistical Learning Theory[M]. New York: Springer, 1995
  • 2Burges C J C. A tutorial on support vector machine for pattern recognition [J]. Knowledge Discovery and Data Mining, 1998, 2(2): 121～167
  • 3Burges C J C, Schlkopf B. Improving the accuracy and speed of support vector learning machines[A]. In: Mozer M, Jordan M, Petsche T, eds. Advances in Neural Information Processing Systems Cambridge[C]. MA: MIT Press, 1997. 375～381
  • 4Nando de Freitas, et al. Sequential support vector machines[A]. In: Proceedings of IEEE INternational Workshop on Neural Networks for Signal Processing, Winsconsin, 1999. 31～40
  • 5Tom Downs, Kevin E Gates, et al. Exact simplification of support vector solutions[OL]. http://www.kernel-machines.org/imlr/, 2001
  • 6Schlkopf B, Mika S, Cburges C J, et al. Input space versus feature space in kernel-based methods[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 1000～1017
  • 7Schlkopf B, Smola A, Müller K-R, et al. Support vector methods in learning and feature extraction[A]. In: Downs T, Frean M, Gallagher M, eds. Proceedings of the 9th Australian Conference on Neural Networks, Brisbane, Australia, 1998. 72～78
  • 8University of California Irvine. Benchmark repository-A collection of artificial and real-world data sets[OL]. http://www.ics.uci.edu/-mlearn
  • 9Chen Longbin. Svm-Chen 2.0[OL]. http://svmlight.joachims.org/

同被引文献6

  • 1VAPNIK V.Statistical learning theory[M].New York:Wiley,1998.
  • 2TRAN Quang-Anh,ZHANG Qian-Li,LI Xing.Reduce the number of support vectors by using clustering techniques[A].Proceedings of the Second International Conference on Machine Learning and Cybernetic,Vol2[C].Xi'an,2003:1245-1248.
  • 3DOWNS T,GATES K E,MASTERS A.Exact simplification of support vectors solutions[J].Journal of Machine Learning Research,2001(2):293-297.
  • 4AMARI S,WU S.Improving support vector machine classifiers by modifying kernel functions[J].Neural Networks,1999,12:783-789.
  • 5KOGGALAGE R,HALGAMUGE S.Reducing the number of training samples for fast support vector machine classification[J].Neural Information Processing,2004,2(3):57-65.
  • 6肖健华.基于支持对象的野点检测方法[J].计算机工程,2003,29(11):43-45. 被引量：23

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计算机辅助设计与图形学学报
2003年 第6期

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