• DocumentCode
    2032972
  • Title

    Using Support Vector Machines for Numerical Prediction

  • Author

    Hussain, Shahid ; Khamisani, Vaqar

  • Author_Institution
    PAF-Karachi Inst. of Economic & Technol., Microsoft Pakistan, Karachi
  • fYear
    2007
  • fDate
    28-30 Dec. 2007
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    We present a new methodology for prediction of numeric data using only classifiers for discrete data such as C4.5/J4.8 and/or support vector machine (SVM). We have used the "one-vs-all" approach to discretize the numeric data into binary and then used support vector machine (SVM) with boosting using free Weka toolkit. Empirical study was carried on real datasets taken from University of California, Irvine (UCI) machine learning repository.
  • Keywords
    mathematics computing; support vector machines; Weka toolkit; numerical prediction; one-vs-all approach; support vector machines; Boosting; Decision trees; Equations; Machine learning; Predictive models; Regression analysis; Regression tree analysis; Support vector machine classification; Support vector machines; Testing; Support vector machine; WEKA; boosting; classification; numerical prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multitopic Conference, 2007. INMIC 2007. IEEE International
  • Conference_Location
    Lahore
  • Print_ISBN
    978-1-4244-1552-6
  • Electronic_ISBN
    978-1-4244-1553-3
  • Type

    conf

  • DOI
    10.1109/INMIC.2007.4557695
  • Filename
    4557695
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2032972