• DocumentCode
    1739149
  • Title

    DirectSVM: a fast and simple support vector machine perceptron

  • Author

    Roobaert, Danny

  • Author_Institution
    Comput. Vision & Active Perception Lab., R. Inst. of Technol., Stockholm, Sweden
  • Volume
    1
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    356
  • Abstract
    We propose a simple implementation of the support vector machine (SVM) for pattern recognition, that is not based on solving a complex quadratic optimization problem. Instead we propose a simple, iterative algorithm that is based on a few simple heuristics. The proposed algorithm finds high-quality solutions in a fast and intuitively-simple way. In experiments on the COIL database, on the extended COIL database and on the Sonar database of the UCI Irvine repository, DirectSVM is able to find solutions that are similar to these found by the original SVM. However DirectSVM is able to find these solutions substantially faster, while requiring less computational resources than the original SVM
  • Keywords
    learning automata; neural nets; pattern recognition; perceptrons; COIL database; DirectSVM; Sonar database; Statistical Learning Theory; high-quality solutions; iterative algorithm; learning algorithms; pattern recognition; support vector machine perceptron; Automatic control; Control systems; Databases; Iterative algorithms; Neural networks; Neurons; Pattern recognition; Quadratic programming; Statistical learning; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop
  • Conference_Location
    Sydney, NSW
  • ISSN
    1089-3555
  • Print_ISBN
    0-7803-6278-0
  • Type

    conf

  • DOI
    10.1109/NNSP.2000.889427
  • Filename
    889427