• DocumentCode
    1567167
  • Title

    Sparse Kernel Regression Modelling Based on L1 Significant Vector Learning

  • Author

    Gao, Junbin ; Shi, Daming

  • Author_Institution
    Sch. of Inf. Technol., Charles Sturt Univ., Bathurst, NSW
  • Volume
    3
  • fYear
    2005
  • Lastpage
    1930
  • Abstract
    A novel L1 significant vector (SV) regression algorithm is proposed in the paper. The proposed regularized L1 SV algorithm finds the significant vectors in a successive greedy process. The performance of the proposed algorithm is comparable to the OLS algorithm while it saves a lot of time complexities in implementing orthogonalization needed in the OLS algorithm
  • Keywords
    identification; least squares approximations; regression analysis; L1 significant vector learning; nonlinear system identification; orthogonal least squares algorithm; sparse kernel regression modelling; Kernel;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    0-7803-9422-4
  • Type

    conf

  • DOI
    10.1109/ICNNB.2005.1615001
  • Filename
    1615001
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1567167