• DocumentCode
    188490
  • Title

    A Comparative Study of Extreme Learning Machine Pruning Based on Detection of Linear Independence

  • Author

    Tavares, L.D. ; Saldanha, R.R. ; Vieira, D.A.G. ; Lisboa, A.C.

  • Author_Institution
    Grad. Program in Electr. Eng., Fed. Univ. of Minas Gerais, Belo Horizonte, Brazil
  • fYear
    2014
  • fDate
    10-12 Nov. 2014
  • Firstpage
    63
  • Lastpage
    69
  • Abstract
    Extreme Learning Machine (ELM) is gaining fairly popularity in training neural networks, due to its simplicity and speed. However, the number of neurons in the hidden layer is still an open problem. This paper proposes a method for pruning the hidden layer neurons based on the linear combination of the hidden layer weights and the input data and compare four methods of detecting linear dependence between vectors.
  • Keywords
    learning (artificial intelligence); neural nets; ELM; extreme learning machine pruning; hidden layer weights; linear independence detection; neural networks; training; Biological neural networks; Complexity theory; Neurons; Null space; Testing; Training; Vectors; Extreme Learning Machines; Hidden layer; Linear dependence; Pruning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence (ICTAI), 2014 IEEE 26th International Conference on
  • Conference_Location
    Limassol
  • ISSN
    1082-3409
  • Type

    conf

  • DOI
    10.1109/ICTAI.2014.20
  • Filename
    6984456
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=188490