• DocumentCode
    390592
  • Title

    Using support vector machines for mining regression classes in large data sets

  • Author

    Sun, Zonghai ; Gao, Lixin ; Sun, Youxian

  • Author_Institution
    Nat. Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China
  • Volume
    1
  • fYear
    2002
  • fDate
    28-31 Oct. 2002
  • Firstpage
    89
  • Abstract
    Support vector machines (SVM) overcome the limit of the maximum-likelihood. method that only applies to very limited set of the density functions. They can estimate simultaneously the regression classes in the mixture data set. The validity of the SVM was demonstrated in experiments. The results indicate that the SVM can estimate the regression classes in the mixture data set with noise.
  • Keywords
    data mining; regression analysis; support vector machines; very large databases; SVM; data mining; large data sets; mixture data set; noise; regression classes; support vector machines; Data mining; Density functional theory; Educational institutions; Industrial control; Kernel; Laboratories; Pattern classification; Polynomials; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering
  • Print_ISBN
    0-7803-7490-8
  • Type

    conf

  • DOI
    10.1109/TENCON.2002.1181221
  • Filename
    1181221
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=390592