• DocumentCode
    1600036
  • Title

    and the perceptron is better than its reputation after all!

  • Author

    Faessler, Angela

  • Author_Institution
    St. Gallen Sch. of Eng., Switzerland
  • fYear
    1996
  • Firstpage
    139
  • Lastpage
    145
  • Abstract
    A large class of functions (in one or more variables) can be approximated by a, generally, multilayer feed-forward network, in which only the weights of the last layer need to be trained. All others can be selected appropriately dependent upon the desired accuracy with which the training examples are to be approximated, but independently of the examples. Thus only a perceptron remains to be trained
  • Keywords
    feedforward neural nets; learning (artificial intelligence); multilayer perceptrons; multilayer feed-forward network; neural network weight training; perceptron; Feedforward systems; Linear regression; Neural networks; Neurons; Polynomials; Zinc;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neuro-Fuzzy Systems, 1996. AT'96., International Symposium on
  • Conference_Location
    Lausanne
  • Print_ISBN
    0-7803-3367-5
  • Type

    conf

  • DOI
    10.1109/ISNFS.1996.603831
  • Filename
    603831