• DocumentCode
    3339386
  • Title

    A Novel Evaluation Method Basing on Support Vector Machines

  • Author

    Xian, Guang-Ming ; Zeng, Bi-qing

  • Author_Institution
    Dept. of Comput. Eng., South China Normal Univ., Foshan
  • fYear
    2008
  • fDate
    24-26 April 2008
  • Firstpage
    316
  • Lastpage
    319
  • Abstract
    Recently support vector machine (SVM) has become a more and more popular classification tool. We presented our two-phase, efficient, and fair evaluation method for DRMs (digital right management system) basing on SVM. Influence of three difference methods and test set number on evaluation result is discussed. After analysized by binary logistic regression, odds ratio comparison of SVM with multi-phase fuzzy synthesized evaluation and FCM illustrates that SVM is the most excellent in these three approaches. Through detailed experimental evaluations under various data set of samples and approaches, our evaluation method of SVM is illustrated to be scalable and accurate.
  • Keywords
    fuzzy set theory; regression analysis; support vector machines; binary logistic regression; digital right management system; fuzzy c-mean; multiphase fuzzy synthesized evaluation; support vector machines; Clustering algorithms; Graphics; Iterative algorithms; Logistics; Machine learning; Pervasive computing; Support vector machine classification; Support vector machines; Testing; Web services; FCM; SVM;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia and Ubiquitous Engineering, 2008. MUE 2008. International Conference on
  • Conference_Location
    Busan
  • Print_ISBN
    978-0-7695-3134-2
  • Type

    conf

  • DOI
    10.1109/MUE.2008.111
  • Filename
    4505742