• DocumentCode
    1264437
  • Title

    The multilayer perceptron as an approximation to a Bayes optimal discriminant function

  • Author

    Ruck, Dennis W. ; Rogers, Steven K. ; Kabrisky, Matthew ; Oxley, Mark E. ; Suter, Bruce W.

  • Author_Institution
    Sch. of Eng., US Air Force Inst. of Technol., Wright-Patterson AFB, OH, USA
  • Volume
    1
  • Issue
    4
  • fYear
    1990
  • fDate
    12/1/1990 12:00:00 AM
  • Firstpage
    296
  • Lastpage
    298
  • Abstract
    The multilayer perceptron, when trained as a classifier using backpropagation, is shown to approximate the Bayes optimal discriminant function. The result is demonstrated for both the two-class problem and multiple classes. It is shown that the outputs of the multilayer perceptron approximate the a posteriori probability functions of the classes being trained. The proof applies to any number of layers and any type of unit activation function, linear or nonlinear
  • Keywords
    neural nets; probability; Bayes optimal discriminant function; backpropagation; classifier; multilayer perceptron; multiple class problems; neural networks; probability; two-class problem; unit activation function; Backpropagation; Bayesian methods; Books; Image analysis; Multi-layer neural network; Multilayer perceptrons; Neural networks; Pattern recognition; Probability density function;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.80266
  • Filename
    80266
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1264437