• DocumentCode
    553095
  • Title

    A new regression method based on SVM classification

  • Author

    Dong Yi ; Cheng Wei ; Li Shengfeng

  • Author_Institution
    Inst. of Appl. Math., Bengbu Coll., Bengbu, China
  • Volume
    2
  • fYear
    2011
  • fDate
    26-28 July 2011
  • Firstpage
    770
  • Lastpage
    773
  • Abstract
    SVM (Support Vector Machines) is a novel algorithm of machine learning which is based on SLT (Statistical Learning Theory). It can solve the problem characterized by nonlinear, high dimension, small sample and local minimizing perfectly. For non-linear problem, the forecasting technique of FCTR (First classification, then regression) was proposed, based on the classification approach of SVM and has carried on the simulation experiment. The experiment shows that the fitting value which obtains using the return to first would be more precise than directly. Using this method to food production forecast, its accuracy is superior to other production forecasting methods.
  • Keywords
    food processing industry; learning (artificial intelligence); pattern classification; production engineering computing; regression analysis; support vector machines; FCTR forecasting technique; SVM classification; first classification then regression; food production forecast; machine learning; regression method; statistical learning theory; support vector machines; Educational institutions; Forecasting; Integrated circuit modeling; Numerical models; Predictive models; Production; Support vector machines; first classification, then regression (FCTR); forecast; support vector machines (SVM);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-61284-180-9
  • Type

    conf

  • DOI
    10.1109/FSKD.2011.6019663
  • Filename
    6019663