• DocumentCode
    2420489
  • Title

    Unconstrained Transductive Support Vector Machines

  • Author

    Tian, Yingjie ; Yan, Manfu

  • Author_Institution
    Chinese Acad. of Sci., Beijing
  • Volume
    2
  • fYear
    2007
  • fDate
    24-27 Aug. 2007
  • Firstpage
    181
  • Lastpage
    185
  • Abstract
    Support vector machines have been extensively used in machine learning because of its efficiency and its theoretical background. This paper focuses on transductive support vector machines (TSVM) for classification and construct a new algorithm - unconstrained transductive support vector machines (UTSVM). After researching on the special construction of primal problem in TSVM, we transform it to an unconstrained problem and then smooth the derived problem in order to apply usual optimization methods.
  • Keywords
    optimisation; support vector machines; machine learning; optimization methods; unconstrained problem; unconstrained transductive support vector machines; Artificial intelligence; Educational institutions; Machine learning; Machine learning algorithms; Medical services; Optimization methods; Predictive models; Static VAr compensators; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on
  • Conference_Location
    Haikou
  • Print_ISBN
    978-0-7695-2874-8
  • Type

    conf

  • DOI
    10.1109/FSKD.2007.599
  • Filename
    4406069
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2420489