• DocumentCode
    3618290
  • Title

    An application of the learning theory to wavelet based signal denoising

  • Author

    M. Stankovic;S. Stankovic

  • Author_Institution
    IRITEL, Belgrade, Serbia
  • fYear
    2004
  • fDate
    6/26/1905 12:00:00 AM
  • Firstpage
    255
  • Lastpage
    259
  • Abstract
    In this paper the statistical learning theory is applied to signal denoising using wavelets. The methodology is based on the estimation of the functional relationship between the Vapnik-Chervonenkis (VC) dimension and approximation complexity. Experimental results confirm the basic assumptions.
  • Keywords
    "Signal denoising","Least squares approximation","Function approximation","Noise reduction","Discrete wavelet transforms","Digital filters","Filtering theory","Fourier transforms","Signal processing","Virtual colonoscopy"
  • Publisher
    ieee
  • Conference_Titel
    Neural Network Applications in Electrical Engineering, 2004. NEUREL 2004. 2004 7th Seminar on
  • Print_ISBN
    0-7803-8547-0
  • Type

    conf

  • DOI
    10.1109/NEUREL.2004.1416588
  • Filename
    1416588
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3618290