Feedback pattern recognition by inverse recall neural network model
Keiji Yamada · 2002
A feedback pattern recognition method based on an inverse recall neural network model is proposed. The model is a kind of multi-layer feedforward model. This model has three functional components. First is a recognition result generation according to a feedforward process in a multi-layer model. Another component calculates an uncertainty score from the output values. If the uncertainty score is greater than a threshold value, the last component, that is an inverse recall, operates. The inverse recall function produces changes in input values, in order to reduce the uncertainty score. The changes show input parts which are important for more certain recognition but are missed in the input pattern. The feedback method can adjust feature extraction parameters so as to detect the important features shown by the inverse recall network model. Then, features are extracted again by using the modified feature extraction parameter values. These feedforward and feedback processings are repeated until a certain recognition result is obtained. This method was examined for handwritten alpha-numeric recognition and it was found that a rejection ratio can be reduced at the same substitution error ratio.>