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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Journal ArticleDOI
A Survey of Selective Ensemble Learning Algorithms: A Survey of Selective Ensemble Learning Algorithms
Chun-Xia Zhang,Jiang-She Zhang +1 more
Proceedings ArticleDOI
Multi-STEPS: Multi-label similarity and truth estimation for propagated segmentations
TL;DR: A new multi-label local ranking strategy for template selection based on the locally normalised cross correlation (LNCC) and an extension to the classical STAPLE algorithm by Warfield et al.
Journal ArticleDOI
Creating and measuring diversity in multiple classifier systems using support vector data description
TL;DR: Analyzing the results shows that the proposed method improves system's overall performance and accuracy in many cases and also measures diversity more precisely.
Book ChapterDOI
Arabic Words Recognition with Classifiers Combination: An Application to Literal Amounts
TL;DR: An approach for recognizing the legal amount for handwritten Arabic bank check is described, which uses multiple information sources to recognize words and obtained results are more interesting than those obtained with individual classifiers.
Proceedings ArticleDOI
Multisensor data fusion for obstacle detection in automated factory logistics
TL;DR: A comparative analysis among different strategies of multisensor data fusion compliant with the requirements of the described system, highlighting their advantages and drawbacks is presented.
References
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