• DocumentCode
    573168
  • Title

    Application of entropic value-at-risk in machine learning with corrupted input data

  • Author

    Ahmadi-Javid, Amir

  • Author_Institution
    Dept. of Ind. Eng., Amirkabir Univ. of Technol., Tehran, Iran
  • fYear
    2012
  • fDate
    2-5 July 2012
  • Firstpage
    1104
  • Lastpage
    1107
  • Abstract
    The entropic value-at-risk (EVaR) is a coherent risk measure that is efficiently computable for the sum of independent random variables. This paper shows how this risk measure can be used in machine learning when uncertainty affects the input data. For this purpose, we consider here a support vector machine with corrupted input data.
  • Keywords
    learning (artificial intelligence); random processes; support vector machines; EVaR; corrupted input data; entropic value-at-risk; machine learning; random variable; risk measure; support vector machine; Linear programming; Machine learning; Optimization; Random variables; Reactive power; Support vector machines; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science, Signal Processing and their Applications (ISSPA), 2012 11th International Conference on
  • Conference_Location
    Montreal, QC
  • Print_ISBN
    978-1-4673-0381-1
  • Electronic_ISBN
    978-1-4673-0380-4
  • Type

    conf

  • DOI
    10.1109/ISSPA.2012.6310455
  • Filename
    6310455
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=573168