• DocumentCode
    534992
  • Title

    Error analysis of classifiers in machine learning

  • Author

    Ding, Lei ; Sheng, Bao-Huai

  • Author_Institution
    Dept. of Math., Shaoxing Univ., Shaoxing, China
  • Volume
    1
  • fYear
    2010
  • fDate
    16-18 Oct. 2010
  • Firstpage
    119
  • Lastpage
    124
  • Abstract
    The paper is related to the error analysis of Support Vector Machine (SVM) classifiers based on reproducing kernel Hilbert spaces. We choose the polynomial kernels as the Mercer kernel and give the error estimate with De La Vallée Poussin means which improve the approximation error. On the other hand, the distortion is replaced by the uniformly boundedness of the Cesàro means. We also introduce the standard estimation of the sample error, and derive the explicit learning rate.
  • Keywords
    Hilbert spaces; error analysis; learning (artificial intelligence); pattern classification; support vector machines; Hilbert spaces; Mercer kernel; SVM; approximation error; classifier; error analysis; machine learning; polynomial kernels; support vector machine; Approximation error; Error analysis; Estimation; Kernel; Polynomials; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image and Signal Processing (CISP), 2010 3rd International Congress on
  • Conference_Location
    Yantai
  • Print_ISBN
    978-1-4244-6513-2
  • Type

    conf

  • DOI
    10.1109/CISP.2010.5646324
  • Filename
    5646324
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=534992