• DocumentCode
    1810485
  • Title

    Preconditioning method to accelerate neural networks gradient training algorithms

  • Author

    Pérez-Ilzarbe, M.J.

  • Author_Institution
    Dept. de Autom. y Comput., Univ. Publica de Navarro, Spain
  • Volume
    2
  • fYear
    1999
  • fDate
    36342
  • Firstpage
    1384
  • Abstract
    In this work a simple method for conditioning neural networks gradient training algorithms is presented. It consists of using a different learning rate for the outgoing weights of each one of the neurons or network input nodes. In the case of one layer neural networks the method can also be implemented by normalizing the input training examples in certain way. The performance of the method proposed has been tested in the training of neural networks to solve a problem of image recognition. A considerable acceleration of the training algorithms has been attained in the examples tested
  • Keywords
    backpropagation; feedforward neural nets; gradient methods; image recognition; accelerated learning; delta rule; error backpropagation; gradient learning algorithms; image recognition; learning rate; multilayer neural networks; Acceleration; Backpropagation algorithms; Convergence; Eigenvalues and eigenfunctions; Image recognition; Multi-layer neural network; Neural networks; Neurons; Testing; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1999. IJCNN '99. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-5529-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.1999.831165
  • Filename
    831165