DocumentCode
276628
Title
An unsupervised training rule for dynamic information processing
Author
Haghighi, Siamack ; Akers, Lex A.
Author_Institution
Intel. Corp., Chandler, AZ, USA
Volume
i
fYear
1991
fDate
8-14 Jul 1991
Firstpage
605
Abstract
The authors have derived an unsupervised training rule for multilayered neural networks. The adaptive weight values are selected to maximize the fraction of a node output variance due to correlations among inputs. The processing units are not required to be fully connected. Applications of this training rule in image processing, including edge extraction, picture segmentation, multirepresentation, and motion detection, are presented
Keywords
computerised picture processing; neural nets; training; adaptive weight values; dynamic information processing; edge extraction; image processing; motion detection; multilayered neural networks; multirepresentation; node output variance; picture segmentation; training rule; unsupervised training rule; Data mining; Image edge detection; Image processing; Image segmentation; Information processing; Motion detection; Neural networks; Pattern recognition; Power measurement; Solid state circuits;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location
Seattle, WA
Print_ISBN
0-7803-0164-1
Type
conf
DOI
10.1109/IJCNN.1991.155247
Filename
155247
Link To Document