• DocumentCode
    1754026
  • Title

    Least Square Regressions with Coefficient Regularization

  • Author

    Peixin, Ye ; Baohuai, Sheng

  • Author_Institution
    Sch. of Math., Nankai Univ., Tianjin, China
  • Volume
    1
  • fYear
    2011
  • fDate
    28-29 March 2011
  • Firstpage
    167
  • Lastpage
    170
  • Abstract
    We consider the least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernel. An explicit expression of the solution of this kernel scheme is derived. Then we provide a sample error with a decay of O(1/√m) and estimate the approximation error in terms of some kind of K-functional.
  • Keywords
    learning (artificial intelligence); least squares approximations; regression analysis; approximation error; coefficient regularization; data dependent hypothesis; least square regression; Approximation error; Computational modeling; Distributed databases; Hilbert space; Kernel; Support vector machines; System-on-a-chip; Coefficient Regularization; Data Dependent Hypothesis; General Kernel; Least Square Regressions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on
  • Conference_Location
    Shenzhen, Guangdong
  • Print_ISBN
    978-1-61284-289-9
  • Type

    conf

  • DOI
    10.1109/ICICTA.2011.50
  • Filename
    5750582
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1754026