US Dollar Classifications by the LVQ Method Based on Reliability Criterion
US Dollar Classifications by the LVQ Method Based on Reliability Criterion Toshihisa Kosaka, Member (Glory Ltd.), Norikazu Taketani, Member (Glory Ltd.), Sigeru Omatu, Member (Osaka Prefecture University), Kunihiro Ryo Non-member (Glory Ltd.) 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.