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
Link To Document