Journal ArticleDOI
Methods of combining multiple classifiers and their applications to handwriting recognition
Lei Xu,Adam Krzyżak,Ching Y. Suen +2 more
- Vol. 22, Iss: 3, pp 418-435
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TLDR
On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly.Abstract:
Possible solutions to the problem of combining classifiers can be divided into three categories according to the levels of information available from the various classifiers. Four approaches based on different methodologies are proposed for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-nearest-neighbor, and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly. For example, on the US zipcode database, 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as high reliability with 95% recognition, 0% substitution, and 5% rejection. >read more
Citations
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A review of information fusion techniques employed in iris recognition systems
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Implementing Dempster's Rule for Hierarchical Evidence.
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TL;DR: This article gives an algorithm for the exact implementation of Dempster’s rule in the case of hierarchical evidence, which is computationally efficient, and makes the approximation suggested by Gordon and Shortliffe unnecessary.
Book ChapterDOI
Texture classification through combination of sequential colour texture classifiers
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Neural structures for visual motion tracking
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Fusion of biometric algorithms in the recognition problem
Andrew L. Rukhin,Igor Malioutov +1 more
TL;DR: The suggested procedures define several versions of aggregated rankings for several biometric algorithms in the recognition or identification problem.
References
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An introduction to hidden Markov models
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