• DocumentCode
    458829
  • Title

    Smoothing Support Vector Machines for e-Insensitive Regressi

  • Author

    Xiong, Jinzhi ; Hu, Tianming ; Hu, Jinlian ; Li, Guangming ; Peng, Hong

  • Author_Institution
    Software Coll., Dongguan Univ. of Technol.
  • Volume
    1
  • fYear
    2006
  • fDate
    16-18 Oct. 2006
  • Firstpage
    222
  • Lastpage
    228
  • Abstract
    Researching smooth support vector machine (SVM) for regression is an active field in data mining. Recently, Lee et al. proposed the smooth SVM for insensitive regression, where smoothing functions play a vital role in smooth SVMs. This paper presents a comparative study on three smooth SVMs: smooth SVM, polynomial smooth SVM and smooth support vector regression. It also discusses promising directions of support vector regression for future work
  • Keywords
    data mining; regression analysis; smoothing methods; support vector machines; data mining; insensitive regression; polynomial smooth SVM; smooth support vector regression; smoothing function; support vector machines; Data mining; Educational institutions; Face detection; Face recognition; Handwriting recognition; Polynomials; Smoothing methods; Statistical learning; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
  • Conference_Location
    Jinan
  • Print_ISBN
    0-7695-2528-8
  • Type

    conf

  • DOI
    10.1109/ISDA.2006.244
  • Filename
    4021439
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=458829