• DocumentCode
    2727253
  • Title

    Complexity regularization using data-dependent penalties

  • Author

    Lugosi, GBbor ; Nobel, Andrew

  • Author_Institution
    Dept. of Math., Tech. Univ. Budapest, Hungary
  • fYear
    1995
  • fDate
    17-22 Sep 1995
  • Firstpage
    254
  • Abstract
    We define a regression function estimate based on complexity regularization, where the list of candidate functions and the corresponding penalties are determined from the training data, leading to improved performance
  • Keywords
    statistical analysis; candidate functions; complexity regularization; data-dependent penalties; regression function estimate; training data; Artificial intelligence; Bismuth; Mathematics; Random variables; Statistics; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory, 1995. Proceedings., 1995 IEEE International Symposium on
  • Conference_Location
    Whistler, BC
  • Print_ISBN
    0-7803-2453-6
  • Type

    conf

  • DOI
    10.1109/ISIT.1995.535769
  • Filename
    535769
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2727253