A study on evaluating and improving the reliability of bank note neuro-classifiers
暂无分享,去创建一个
This paper addresses the reliability of the bank note classifiers and a new method is proposed for improving the classification reliability based on the local principal components analysis (PCA). The reliability is evaluated by using an algorithm, which employs a function of winning class probability and second maximal probability in the LVQ classifier. The experimental results from 3,600 data samples show an increase up to 100% in the reliability of classification.
[1] M. Kramer. Nonlinear principal component analysis using autoassociative neural networks , 1991 .
[2] Sigeru Omatu,et al. Classification of Italian Bills by a Competitive Neural Network , 1999 .
[3] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[4] Marco Gori,et al. A neural network-based model for paper currency recognition and verification , 1996, IEEE Trans. Neural Networks.