@inbook{ea866f481eb243468281c26ce935735f,
title = "Combining the results of several neural network classifiers",
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.",
keywords = "Character recognition, Classifier, Evidence, Neural network, The Dempster-Shafer theory of evidence",
author = "Galina Rogova",
year = "2008",
doi = "10.1007/978-3-540-44792-4\_27",
language = "English",
isbn = "9783540253815",
series = "Studies in Fuzziness and Soft Computing",
pages = "683--692",
editor = "Yager, \{Roland R.\} and Liping Liu",
booktitle = "Classic Works of the Dempster-Shafer Theory of Belief Functions",
}