By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
B. R. Linnell ; Center for Robotics & Intelligent Machines, North Carolina State Univ., Raleigh, NC, USA

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

Published in:

Neural Networks, 1999. IJCNN '99. International Joint Conference on  (Volume:3 )

Date of Conference:

1999