• DocumentCode
    2769097
  • Title

    Analysis of the backpropagation algorithm using linear algebra

  • Author

    De Sousa, Celso André Rodrigues

  • Author_Institution
    Inst. of Math. & Comput. Sci. (ICMC), Univ. of Sao Paulo (USP), Sao Carlos, Brazil
  • fYear
    2012
  • fDate
    10-15 June 2012
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    Multilayer perceptrons (MLPs) are feed-forward artificial neural networks with high theoretical basis. The most popular algorithm to train MLPs is the backpropagation algorithm, which can be seen as a consistent nonparametric least squares regression estimator. This algorithm is reformulated in this paper using linear algebra, providing theoretical basis for further studies.
  • Keywords
    backpropagation; feedforward neural nets; learning (artificial intelligence); least squares approximations; linear algebra; multilayer perceptrons; regression analysis; MLP training; backpropagation algorithm; consistent nonparametric least squares regression estimator; feed-forward artificial neural networks; linear algebra; multilayer perceptrons; Backpropagation algorithms; Biological neural networks; Logistics; Neurons; Training; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2012 International Joint Conference on
  • Conference_Location
    Brisbane, QLD
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4673-1488-6
  • Electronic_ISBN
    2161-4393
  • Type

    conf

  • DOI
    10.1109/IJCNN.2012.6252364
  • Filename
    6252364
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2769097