• DocumentCode
    1547798
  • Title

    The relevance vector machine technique for channel equalization application

  • Author

    Chen, S. ; Gunn, S.R. ; Harris, C.J.

  • Author_Institution
    Dept. of Electron. & Comput. Sci., Southampton Univ., UK
  • Volume
    12
  • Issue
    6
  • fYear
    2001
  • fDate
    11/1/2001 12:00:00 AM
  • Firstpage
    1529
  • Lastpage
    1532
  • Abstract
    The relevance vector machine (RVM) technique is applied to communication channel equalization. It is demonstrated that the RVM equalizer can closely match the optimal performance of the Bayesian equalizer, with a much sparser kernel representation than that is achievable by the state-of-art support vector machine (SVM) technique
  • Keywords
    Gaussian noise; equalisers; learning automata; white noise; Bayesian equalizer; channel equalization; kernel representation; optimal performance; relevance vector machine technique; support vector machine technique; Bayesian methods; Communication channels; Design optimization; Equalizers; Kernel; Machine learning; Neural networks; Statistical learning; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.963792
  • Filename
    963792
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1547798