• DocumentCode
    1749083
  • Title

    A novel concept for first order learning algorithm design

  • Author

    Geczy, Peter ; Usui, Shiro

  • Author_Institution
    RIKEN, Inst. of Phys. & Chem. Res., Saitama, Japan
  • Volume
    1
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    382
  • Abstract
    One of the essential problems in the neural network field is the fact that some learning techniques perform well on certain classes of problems and fail on the others. Conventional approaches to training neural networks overlook the important link between the learning algorithm and the learning task. Ignoring such evidence leads to various controversies. To resolve the issue requires us to establish a suitable classification framework for both learning algorithms and learning tasks
  • Keywords
    convergence; learning (artificial intelligence); multilayer perceptrons; optimisation; classification framework; first order learning algorithm design; learning task; neural network training; Algorithm design and analysis; Biological neural networks; Convergence; Joining processes; Laboratories; Neuroscience; Optimization methods; Search methods; Stability; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7044-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2001.939050
  • Filename
    939050