• DocumentCode
    701315
  • Title

    RBF networks for density estimation

  • Author

    Sardo, Lucia ; Kittler, Josef

  • Author_Institution
    Department of Electronic & Electrical Engineering, University of Surrey, Guildford, Surrey GU2 5XH, United Kingdom
  • fYear
    1996
  • fDate
    10-13 Sept. 1996
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    A non-parametric probability density function (pdf) estimation technique is presented. The estimation consists in approximating the unknown pdf by a network of Gaussian Radial Basis Functions (GRBFs). Complexity analysis is introduced in order to select the optimal number of GRBFs. Results obtained on real data show the potentiality of this technique.
  • Keywords
    Complexity theory; Computer architecture; Estimation; Iris; Neurons; Probability density function; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    European Signal Processing Conference, 1996. EUSIPCO 1996. 8th
  • Conference_Location
    Trieste, Italy
  • Print_ISBN
    978-888-6179-83-6
  • Type

    conf

  • Filename
    7083041
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=701315