• DocumentCode
    1694699
  • Title

    Research on ECOC SVMs

  • Author

    Yan, Zhigang

  • Author_Institution
    Key Lab. for Land Environ. & Disaster Monitoring of SBSM, China Univ. of Min. & Technol., Xuzhou, China
  • fYear
    2010
  • Firstpage
    2838
  • Lastpage
    2842
  • Abstract
    The generalization performance of ECOC SVMs was analyzed, as a result, the performance of ECOC SVMs is mainly determined by the SVM classifiers corresponding to its codewords, litttlely by its mathematical characteristics. The performance of each SVM is ordered by their separating margins and cross-validation error ratios. Three types of ECOC SVMs, whose performance are better, worse and common, are constructed by selecting different set of SVMs, the ECOC SVMs with a better performance might be constructed by the set of SVMs whose performances are better too, otherwise, its performance might be worse, which supports our viewpoint effectively and points out the direction for improving ECOC SVMs.
  • Keywords
    error correction codes; pattern classification; support vector machines; ECOC SVM; SVM classifiers; error correcting output codes; support vector machine; Informatics; Learning; Machine learning; Monitoring; Pattern recognition; Support vector machines; ECOC SVMs; Generalization Performance; SVM;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2010 8th World Congress on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-1-4244-6712-9
  • Type

    conf

  • DOI
    10.1109/WCICA.2010.5554718
  • Filename
    5554718