Abstract
In subject classification, artificial neural networks (ANNS) are efficient and objective classification methods. Thus, they have been successfully applied to the numerous classification fields. Sometimes, however, classifications do not match the real world, and are subjected to errors. These problems are caused by the nature of ANNS. We discuss these on multilayer perceptron neural networks. By studying of these problems, it helps us to have a better understanding on its classification.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (No. 40771044) and Zhejiang Provincial Science and Technology Foundation of China (No. 2006C23066). We would like to thank the editor and reviewers for their comments that improved the article.
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Feng, L., Hong, W. Classification error of multilayer perceptron neural networks. Neural Comput & Applic 18, 377–380 (2009). https://doi.org/10.1007/s00521-008-0188-0
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DOI: https://doi.org/10.1007/s00521-008-0188-0