Title :
Quadrant-distance graphs: a method for visualizing neural network weight spaces
Author_Institution :
Center for Robotics & Intelligent Machines, North Carolina State Univ., Raleigh, NC, USA
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;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.832624