• DocumentCode
    1842366
  • Title

    Quadrant-distance graphs: a method for visualizing neural network weight spaces

  • Author

    Linnell, B.R.

  • Author_Institution
    Center for Robotics & Intelligent Machines, North Carolina State Univ., Raleigh, NC, USA
  • Volume
    3
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    1666
  • Abstract
    One of the major drawbacks to neural networks is the inability of the user to understand what is happening inside the network. Quadrant-distance (QD) graphs allow the user to graphically display a network´s weight vector at any point in training, for networks of any size. This allows the user to quickly and easily identify similarities or differences between solution sets. QD graphs may also be used for a variety of other analysis functions, such as comparing initial weights to final weights, and observing the path of the network as it finds a solution
  • Keywords
    data visualisation; graph theory; learning (artificial intelligence); neural nets; learning; neural networks; quadrant-distance graphs; weight space visualisation; weight vector; Displays; Extraterrestrial measurements; Intelligent networks; Intelligent robots; Machine intelligence; Neural networks; Orbital robotics; Problem-solving; Testing; Visualization;
  • 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.1999.832624
  • Filename
    832624