• DocumentCode
    1559324
  • Title

    Bounds on the number of samples needed for neural learning

  • Author

    Mehrotra, Kishan G. ; Mohan, Chilukuri K. ; Ranka, Sanjay

  • Author_Institution
    Sch. of Comput. & Inf. Sci., Syracuse Univ., NY, USA
  • Volume
    2
  • Issue
    6
  • fYear
    1991
  • fDate
    11/1/1991 12:00:00 AM
  • Firstpage
    548
  • Lastpage
    558
  • Abstract
    The relationship between the number of hidden nodes in a neural network, the complexity of a multiclass discrimination problem, and the number of samples needed for effect learning are discussed. Bounds for the number of samples needed for effect learning are given. It is shown that Ω(min (d,n) M) boundary samples are required for successful classification of M clusters of samples using a two-hidden-layer neural network with d-dimensional inputs and n nodes in the first hidden layer
  • Keywords
    computational complexity; learning systems; neural nets; pattern recognition; boundary samples; classification; complexity; hidden nodes; multiclass discrimination; neural learning; pattern recognition; two-hidden-layer neural network; Information science; Multi-layer neural network; Neural networks; Performance evaluation; System testing;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.97932
  • Filename
    97932
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1559324