• DocumentCode
    2286660
  • Title

    Incremental learning algorithm of least squares support vector machines based on Renyi entropy

  • Author

    Zhao Guan-hua ; Hao Min

  • Author_Institution
    Sch. of Manage., Tianjin Univ., Tianjin, China
  • fYear
    2009
  • fDate
    14-16 Sept. 2009
  • Firstpage
    95
  • Lastpage
    100
  • Abstract
    This paper introduces Renyi entropy and incremental learning algorithm into the financial distress prediction of enterprises and advances an incremental learning algorithm on least squares support vector machine based on Renyi entropy. By analysis and comparison of LS-SVM incremental learning algorithm based on Renyi entropy,traditional LS-SVM algorithm and standard SVM algorithm,LS-SVM incremental learning algorithm based on the Renyi entropy are evidently superior to the traditional LS-SVM algorithm and the standard SVM in terms of the number of training samples and computing time. This confirms the effectiveness and superiority of the introduction of Renyi entropy into the field of financial distress prediction.
  • Keywords
    entropy; finance; learning (artificial intelligence); least squares approximations; support vector machines; LS-SVM method; Renyi entropy; enterprises financial distress prediction; incremental learning algorithm; least squares support vector machine; Algorithm design and analysis; Conference management; Entropy; Financial management; Forward contracts; Least squares methods; Machine learning; Machine learning algorithms; Quadratic programming; Support vector machines; LS-SVM; Renyi entropy; factor analysis; financial distress prediction; incremental learning algorithm; standard SVM;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management Science and Engineering, 2009. ICMSE 2009. International Conference on
  • Conference_Location
    Moscow
  • Print_ISBN
    978-1-4244-3970-6
  • Electronic_ISBN
    978-1-4244-3971-3
  • Type

    conf

  • DOI
    10.1109/ICMSE.2009.5317570
  • Filename
    5317570