• DocumentCode
    578101
  • Title

    New smooth support vector machine for regression

  • Author

    Shen, Jin-Dong

  • Author_Institution
    Coll. of Sci., China Jiliang Univ., Hangzhou, China
  • Volume
    1
  • fYear
    2012
  • fDate
    15-17 July 2012
  • Firstpage
    310
  • Lastpage
    314
  • Abstract
    Researching smooth support vector machine for regression (SSVR) is an active field in data mining. In this study, a new method that multiple knot spline function is used to make smooth the model of support vector machine for regression is presented. A Multiple Knot Spline SSVR (MKS-SSVR) is obtained. Moreover, by analyzing the function precision, MKS-SSVR is better than SSVR and PSSVR.
  • Keywords
    data mining; regression analysis; splines (mathematics); support vector machines; MKS-SSVR; data mining; function precision; multiple knot spline function; smooth support vector machine for regression; Abstracts; Convergence; Kernel; Regression; Smoothing; Support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
  • Conference_Location
    Xian
  • ISSN
    2160-133X
  • Print_ISBN
    978-1-4673-1484-8
  • Type

    conf

  • DOI
    10.1109/ICMLC.2012.6358931
  • Filename
    6358931
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=578101