登录 | 注册 | 充值 | 退出 | 公司首页 | 繁体中文 | 满意度调查
综合馆
最小二乘隐空间支持向量机
  • 摘要

    在隐空间中采用最小二乘损失函数,提出了最小二乘隐空间支持向量机(LSHSSVMs).同隐空间支持向量机(HSSVMs)一样,最小二乘隐空间支持向量机不需要核函数满足正定条件,从而扩展了支持向量机核函数的选择范围.由于采用了最小二乘损失函数,最小二乘隐空间支持向量机产生的优化问题为无约束凸二次规划,这比隐空间支持向量机产生的约束凸二次规划更易求解.仿真实验结果表明所提算法在计算时间和推广能力上较隐空间支持向量机存在一定的优势.

  • 作者

    王玲  薄列峰  刘芳  焦李成  WANG Ling  BO Lie-Feng  LIU Fang  JIAO Li-Cheng 

  • 作者单位

    西安电子科技大学智能信息处理研究所,西安,710071/西安电子科技大学计算机学院,西安,710071

  • 刊期

    2005年8期 ISTIC EI PKU

  • 关键词

    最小二乘隐空间支持向量机  隐空间支持向量机  支持向量机  最小二乘支持向量机  核函数 

参考文献
  • [1] BURGES C J C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998,02
  • [2] SMOLA A J;Schlkopf B. A tutorial on support vector regression. Royal Holloway College. University of London,UK,NeuroCOLT Technical Report:NC-TR-98-030, 1998
  • [3] Müller K R;Smola A J;Rtsch G. Predicting time series with support vector machines. Cambridge,MA:MIT Press, 1999
  • [4] Zhang Li;Zhou Wei-Da;Jiao Li-Cheng. Hidden space support vector machines. IEEE Transactions on Neural Networks, 2004,06
  • [5] Suykens J A K;Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999,03
  • [6] Hornik K;Stinchcombe M;White H. Multi-layer feed-forward networks are universal approximators. Neural Networks, 1989,05
  • [7] Mhaskar H N;Micchelli C A. Approximation by superposition of a sigmoidal function and radial basis functions. Advances in Applied Mathematics, 1992,03
  • [8] Blanz V;Schlkopf B;Vapnik V N. Extracting support data for a given task. Menlo Park,CA:AAAI Press, 1995
  • [9] 袁亚湘;孙文瑜. 最优化理论与方法. 北京:科学出版社, 1997
  • [10] Suykens J A K;Lukas L;Dooren P Van;Moor B. De. Least squares support vector machines classifiers: A large scale algorithm. Stresa,Italy, 1999
  • [11] Patrick P. Minimization methods for training feed-forward neural networks. Neural Networks, 1994,01
  • [12] MOLLER M F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 1993,04
  • [13] Blake C L;Merz C J. UCI repository of machine learning databases. Department of Information and Computer Science. University of California,Irvine,CA, 1998
  • [14] Michie D;Spiegelhalter D J;Taylor C C. Machine Learning, Neural and Statistical Classification. Englewood Cliffs:Prentice Hall, 1994
  • [15] ARONSZAJN N. Theory of reproducing kernels. Transactions of the American Mathematical Society, 1950
  • [16] 张学工. 统计学习理论的本质. 北京:清华大学出版社, 2000
  • [17] Hsu C W;Lin C J. A comparison of methods for Multi-class support vector machines. IEEE Transactions on Neural Networks, 2002,02
查看更多︾
相似文献 查看更多>>
54.81.69.220