Abstract
Neural networks and traditional classifiers work well for optical character recognition; however, it is advantageous to combine the results of several algorithms to improve classification accuracies. This paper presents a combination method based on the Dempster–Shafer theory of evidence, which uses statistical information about the relative classification strengths of several classifiers. Numerous experiments show the effectiveness of this approach. The method allows 15—30% reduction of misclassification error compared to the best individual classifier.
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Rogova, G. (2008). Combining the Results of Several Neural Network Classifiers. In: Yager, R.R., Liu, L. (eds) Classic Works of the Dempster-Shafer Theory of Belief Functions. Studies in Fuzziness and Soft Computing, vol 219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44792-4_27
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DOI: https://doi.org/10.1007/978-3-540-44792-4_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25381-5
Online ISBN: 978-3-540-44792-4
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