• DocumentCode
    1562926
  • Title

    Performance analysis and comparison of neural networks and support vector machines classifier

  • Author

    Zheng, Enhui ; Li, Ping ; Song, Zhihuan

  • Author_Institution
    Inst. of Ind. Process Control, Zhejiang Univ., Hangzhou, China
  • Volume
    5
  • fYear
    2004
  • Firstpage
    4232
  • Abstract
    The theory foundation and classification algorithm of neural networks (NN) and support vector machines (SVM) are researched and compared from their conceptual constructs to basic mathematical reasons, on the basis of which the SVM classification system and the NN classification system are constructed respectively. The performances of the two classification systems are tested on two sets of benchmark data, and the SVM classification system shows better performance in binary classification tasks.
  • Keywords
    learning (artificial intelligence); minimisation; neural nets; pattern classification; support vector machines; SVM classification system; binary classification tasks; classification algorithm; learning algorithm; mathematical reasons; minimization; neural network classification system; performance analysis; support vector machines; Least squares approximation; Neural networks; Pattern recognition; Performance analysis; Risk management; Statistical learning; Support vector machine classification; Support vector machines; System testing; Virtual colonoscopy;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
  • Print_ISBN
    0-7803-8273-0
  • Type

    conf

  • DOI
    10.1109/WCICA.2004.1342308
  • Filename
    1342308