Inverse recall neural network model and feedback pattern recognition
Keiji Yamada · 2002
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.>