Title :
Focus of attention in a neural network using meta knowledge
Author :
Hudson, D.L. ; Cohen, M.E.
Author_Institution :
California Univ., San Francisco, CA, USA
Abstract :
An aspect that appears to be of great importance in human decision making is focus of attention. This focus determines the level of detail that should be considered in addressing the current situation. Classification neural networks as they currently exist generally rely on building an overall model based on the data presented. Implementation of a level of detail structure depends on hierarchical modeling. Neural networks at each level of detail must be trained separately, with each requiring different data sets for training and testing. In addition, a method for deciding which level is appropriate must be developed. In the work described in this paper, meta knowledge, a technique derived from knowledge-based reasoning, is used for transition between multiple levels. The meta knowledge described internally structures transitions among the neural network layers
Keywords :
data structures; inference mechanisms; knowledge based systems; learning (artificial intelligence); meta data; neural nets; data structure; focus of attention; human decision making; knowledge-based reasoning; learning; meta knowledge; neural network; Biological neural networks; Buildings; Decision making; Humans; Intelligent networks; Lakes; Nervous system; Neural networks; Roads; Testing;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.857820