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 :
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