• DocumentCode
    3037998
  • Title

    Convergence of coefficient regularized fully online algorithm

  • Author

    Tian, Ming-Dang ; Sheng, Bao-Huai

  • Author_Institution
    Dept. of Math., Shaoxing Univ., Shaoxing, China
  • fYear
    2011
  • fDate
    26-28 July 2011
  • Firstpage
    2059
  • Lastpage
    2065
  • Abstract
    Abstract-This paper gives the convergence of coefficient regularized fully online nonsmooth classification algorithm. With the strongly convex loss function based on the Euclidean Space and the parameter λt changes with learning step give a better convergence rate than the usual convex loss functions.
  • Keywords
    convex programming; learning (artificial intelligence); pattern classification; Euclidean Space; coefficient regularized fully online algorithm convergence; convex loss function; machine learning method; nonsmooth classification algorithm; Approximation algorithms; Classification algorithms; Convergence; Equations; Hilbert space; Kernel; Machine learning algorithms; binary classification; convergence analysis; learning rates; online algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia Technology (ICMT), 2011 International Conference on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-61284-771-9
  • Type

    conf

  • DOI
    10.1109/ICMT.2011.6002468
  • Filename
    6002468
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3037998