• DocumentCode
    1161907
  • Title

    A universal model based on minimax average divergence

  • Author

    Lu, Cheng-Chang ; Dunham, J.G.

  • Author_Institution
    Dept. of Math. Sci., Kent State Univ., OH, USA
  • Volume
    38
  • Issue
    1
  • fYear
    1992
  • fDate
    1/1/1992 12:00:00 AM
  • Firstpage
    140
  • Lastpage
    144
  • Abstract
    Given a set of training samples, the commonly used approach to determine a universal model is accomplished by averaging the statistics over all training samples. It is suggested to use average divergence as a measurement for the effectiveness of a universal model and a minimax universal model that minimizes the maximum average divergence among all training samples is proposed. Efficient searching algorithms are developed and experimental results are presented
  • Keywords
    data compression; encoding; information theory; minimax techniques; data compression; information theory; minimax average divergence; searching algorithms; source coding; training samples; universal model; Context modeling; Data compression; Encoding; Entropy; Information theory; Minimax techniques; Performance analysis; Source coding; Statistics; Stochastic processes;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/18.108259
  • Filename
    108259
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1161907