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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Proceedings ArticleDOI
Subword-based text-dependent speaker verification system with user-selectable passwords
M. Sharma,Richard J. Mammone +1 more
TL;DR: A subword-based, text-dependent speaker verification system that embodies the capability of user-selectable passwords (ideally, with no constraints on the choice of vocabulary words or the language), and a novel automatic speech segmentation procedure, called the "blind" segmentation, is introduced.
Proceedings ArticleDOI
An adaptive weighted majority vote rule for combining multiple classifiers
TL;DR: A novel multiple classifier system that incorporates a global optimization technique based on a genetic algorithm for configuring the system and exhibits better performance than those of the alternative one, due to a better estimate of the reliability of the participating classifiers.
Proceedings ArticleDOI
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Xiaoou Tang,Zhifeng Li +1 more
TL;DR: A multiple classifiers fusion based video face recognition algorithm that preserves all the spatial-temporal information contained in a video sequence.
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Data classification using the Dempster-Shafer method
TL;DR: The results suggest that the D–S approach provides a suitable framework for the design of classification systems and that automating the mass function design and calculation would increase the viability of the algorithm for complex classification problems.
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Improved computation of beliefs based on confusion matrix for combining multiple classifiers
L. Chen,Hongying Lilian Tang +1 more
TL;DR: This work presents a better algorithm with a more generic capability, showing improved performance in beliefs of confidence computation for multiple classifier combination.
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