• DocumentCode
    2414446
  • Title

    Cascade Jump Support Vector Machine Classifiers

  • Author

    Ravindran, Sourabh ; Anderson, David V. ; Rehg, James

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA
  • fYear
    2005
  • fDate
    28-28 Sept. 2005
  • Firstpage
    135
  • Lastpage
    139
  • Abstract
    In this paper we present a new support vector machine (SVM) based classifier that is able to achieve better generalization as compared to the standard SVM. Better generalization is achieved by using a cascade of modified proximal SVMs to remove simpler examples before presenting the difficult examples to a more complex SVM. The cascade structure uses the discrimination afforded by different feature spaces (by using different kernels) to simplify the classification task
  • Keywords
    feature extraction; generalisation (artificial intelligence); pattern classification; support vector machines; cascade jump support vector machine classifier; cascade structure; feature space; generalization; modified proximal SVM; pattern classification; Educational institutions; Kernel; Polynomials; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning for Signal Processing, 2005 IEEE Workshop on
  • Conference_Location
    Mystic, CT
  • Print_ISBN
    0-7803-9517-4
  • Type

    conf

  • DOI
    10.1109/MLSP.2005.1532888
  • Filename
    1532888
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2414446