• DocumentCode
    2711925
  • Title

    Creating an ensemble of diverse support vector machines using Adaboost

  • Author

    Lima, Naiyan Hari Candido ; Neto, Adriao Duarte Doria ; De Melo, Jorge Dantas

  • Author_Institution
    Dept. of Comput. Eng. & Autom., Univ. Fed. do Rio Grande do Norte, Rio Grande, Brazil
  • fYear
    2009
  • fDate
    14-19 June 2009
  • Firstpage
    1802
  • Lastpage
    1806
  • Abstract
    Support vector machines are one of the most employed methods of pattern classification, and the Adaboost algorithm is an effective way of improving the performance of the weak learners that compose the ensemble. In this article, we propose to create an Adaboost-based ensemble of SVM, by altering the Gaussian width parameter of the RBF-SVM. Using data sets from the UCI repository, we made tests to evaluate the algorithm.
  • Keywords
    Gaussian processes; learning (artificial intelligence); pattern classification; radial basis function networks; support vector machines; Adaboost-based ensemble algorithm; Gaussian width parameter; RBF-SVM; diverse support vector machine learning; pattern classification; Boosting; Diversity reception; Error analysis; Kernel; Machine learning algorithms; Neural networks; Pattern classification; Risk management; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2009. IJCNN 2009. International Joint Conference on
  • Conference_Location
    Atlanta, GA
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-3548-7
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2009.5178915
  • Filename
    5178915
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2711925