• DocumentCode
    2324517
  • Title

    Least squares support vector machine ensemble

  • Author

    Bing-Yu Sun ; De-Shuang Huang

  • Author_Institution
    Hefei Institute of Intelligent Machines, Chinese Academy of Sciences
  • Volume
    3
  • fYear
    2004
  • fDate
    25-29 July 2004
  • Firstpage
    2013
  • Abstract
    The LS-SVM ensemble is proposed to improve the performance of the single LS-SVM. During the constructing of the LS-SVM ensemble, bagging algorithm is used because it is more suitable than boosting algorithm in high noise regime. Furthermore, in This work a novel aggregation method of the LS-SVM ensemble is also proposed. Traditionally the aggregation of the ensemble always uses all the available individual LS-SVM, while our approach can exclude the ones which may degrade the performance of the ensemble. Finally, the simulating results demonstrate the effectiveness and efficiency of our approach.
  • Keywords
    least squares approximations; optimisation; pattern classification; support vector machines; SVM ensemble; aggregation method; bagging algorithm; boosting algorithm; least squares support vector machine; optimisation; pattern classification; Automation; Cost function; Degradation; Erbium; Lagrangian functions; Least squares methods; Machine intelligence; Neural networks; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-8359-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2004.1380924
  • Filename
    1380924