• DocumentCode
    552528
  • Title

    Multi-class support vector machines based on the mahalanobis distance

  • Author

    Wang, Heng-you ; Gao, Yan-fei ; Zhang, Chang-lun

  • Author_Institution
    Sch. of Sci., Beijing Univ. of Civil Eng. & Archit., Beijing, China
  • Volume
    2
  • fYear
    2011
  • fDate
    10-13 July 2011
  • Firstpage
    757
  • Lastpage
    762
  • Abstract
    In the last decade, Support vector machine (SVM) has been deeply investigated and it is often used in Hilbert space by the measure of Euclidean distance. In this paper, we present the SVM with mahalanobis distance, and the details of how to compute the mahalanobis distance in the input and the feature space are described. Finally, we apply it to the image classification and compare the results of them. By this, we obtain a sound conclusion.
  • Keywords
    Hilbert spaces; image classification; support vector machines; Euclidean distance; Hilbert space; image classification; mahalanobis distance; multiclass support vector machines; Accuracy; Euclidean distance; Kernel; Machine learning; Remote sensing; Support vector machines; Training; Image Classification; Mahalanobis Distance; Multi-class; Support Vector Machine; kernel;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics (ICMLC), 2011 International Conference on
  • Conference_Location
    Guilin
  • ISSN
    2160-133X
  • Print_ISBN
    978-1-4577-0305-8
  • Type

    conf

  • DOI
    10.1109/ICMLC.2011.6016824
  • Filename
    6016824
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=552528