• DocumentCode
    3413742
  • Title

    Support vector machines for company failure prediction

  • Author

    Yang, Zheng Rong

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Exeter Univ., UK
  • fYear
    2003
  • fDate
    20-23 March 2003
  • Firstpage
    47
  • Lastpage
    54
  • Abstract
    This paper applies support vector machines (SM), a new powerful learning algorithm, to company failure prediction based on 2048 UK construction companies. The study shows that the SVM model outperforms linear statistical models and other neural network models.
  • Keywords
    construction industry; financial data processing; learning (artificial intelligence); learning automata; neural nets; statistical analysis; SVM; company failure prediction; construction companies; learning algorithm; linear statistical models; neural network models; support vector machines; Artificial neural networks; Computer science; Costs; Ear; Failure analysis; Machine learning; Neural networks; Power engineering and energy; Predictive models; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on
  • Print_ISBN
    0-7803-7654-4
  • Type

    conf

  • DOI
    10.1109/CIFER.2003.1196241
  • Filename
    1196241
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3413742