DocumentCode
1903458
Title
Inverse recall neural network model and feedback pattern recognition
Author
Yamada, Keiji
Author_Institution
NEC Corp., Kawasaki, Japan
fYear
1993
fDate
1993
Firstpage
399
Abstract
An inverse recall neural network model and a feedback pattern recognition method based on the model are proposed. The inverse recall neural network model is trained by the same method as that used for a typical multilayer feedforward model. The model can produce an inverse mapping of the trained feedforward mappings to show the parts of an input pattern. The model is applied to the feedback recognition method which can extract features from input patterns and discriminates between them by the inverse recall neural network model. The feedback recognition method adjusts feature extraction parameters so as to detect the important parts shown by the neural network model in order to present them to the network, and to produce more certain recognition results. This method is examined on handwritten alpha-numerics. It is found that rejection ratio can be reduced by half at the same error ratio
Keywords
character recognition; feature extraction; feedforward neural nets; feature extraction parameters; feedback pattern recognition; handwritten alpha-numerics; input patterns; inverse mapping; inverse recall neural network model; recognition results; rejection ratio; same error ratio; trained feedforward mappings; Character recognition; Feature extraction; Information technology; Inverse problems; Laboratories; Multi-layer neural network; National electric code; Neural networks; Neurofeedback; Pattern recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993., IEEE International Conference on
Conference_Location
San Francisco, CA
Print_ISBN
0-7803-0999-5
Type
conf
DOI
10.1109/ICNN.1993.298590
Filename
298590
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