US Dollar Classifications by the LVQ Method Based on Reliability Criterion
Toshihisa Kosaka, Norikazu Taketani, Sigeru Omatu, Kunihiro Ryo · IEEJ Transactions on Electronics Information and Systems · 1999
Automatic classification of bill money has been well developed and it is important that the classifier must have high accuracy. Generally, accuracy of classification is represented as a recognition rate of sample data. To evaluate the accuracy more strictly, we will introduce a reliability criterion.In the pattern recognition, Neural Networks (NNs) have been adopted. Among them a competitive NN has a simple structure and can explain the relation between the inputs and the outputs more easily than a layered NN based on the back-propagation method. Thus, we use a competitive NN for the bill money classification and use the Learning Vector Quantization (LVQ) method for training the NN.After introducing a reliability criterion based on a probability distribution for the classification by the LVQ method, we classify the US dollar by the LVQ method and show the effectiveness of the proposed method.