• DocumentCode
    1592748
  • Title

    Learning Rate of Least Square Regressions with Some Kind of Mercer Kernel

  • Author

    Baohui Sheng ; Liqin Duan ; Peixin Ye

  • Author_Institution
    Dept. of Math., Shaoxing Coll. of Arts & Sci., Shaoxing, China
  • fYear
    2012
  • Firstpage
    329
  • Lastpage
    332
  • Abstract
    We consider the error estimate of least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernel. When the kernel belongs to some kind of Mercer kernel, under a mild regularity condition on the regression function, we derive a dimensional-free learning rate m-1/6.
  • Keywords
    learning (artificial intelligence); least squares approximations; regression analysis; Mercer kernel; coefficient regularization algorithms; data dependent hypothesis; general kernel; learning rate; least square regressions; Convergence; Educational institutions; Eigenvalues and eigenfunctions; Kernel; Least squares approximation; Machine learning; Coeffi Data Dependent Hypothesis; Learning Rate Introduction; Mercer Kernel; Square Regressions; cient Regularization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent System Design and Engineering Application (ISDEA), 2012 Second International Conference on
  • Conference_Location
    Sanya, Hainan
  • Print_ISBN
    978-1-4577-2120-5
  • Type

    conf

  • DOI
    10.1109/ISdea.2012.633
  • Filename
    6173215
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1592748