• DocumentCode
    1979805
  • Title

    Using statistical learning theory for modeling the uncertainty in business and engineering systems: a qualitative introduction

  • Author

    Guergachi, A. Aziz

  • Author_Institution
    Sch. of Inf. Technol. Manage., Ryerson Polytech. Inst. Univ, Toronto, Ont., Canada
  • Volume
    1
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    423
  • Abstract
    Presents a qualitative introduction and justification of the application of statistical learning theory to uncertainty modeling in business and engineering systems. Using simple mathematical tools and metaphorical images, the main variables that govern the uncertainty in a physical system are defined. A general expression of uncertainty models is then obtained. The structure of this expression is the same as that of the uncertainty models that have been developed by rigorously applying the results of statistical learning theory
  • Keywords
    commerce; corporate modelling; engineering; learning (artificial intelligence); statistics; uncertain systems; business systems; engineering systems; mathematical tools; metaphorical images; physical systems; statistical learning theory; uncertainty modeling; Engineering management; Filtration; Human resource management; Management training; Mathematical model; Power system management; Power system modeling; Statistical learning; Systems engineering and theory; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man, and Cybernetics, 2001 IEEE International Conference on
  • Conference_Location
    Tucson, AZ
  • ISSN
    1062-922X
  • Print_ISBN
    0-7803-7087-2
  • Type

    conf

  • DOI
    10.1109/ICSMC.2001.969849
  • Filename
    969849