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
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Book ChapterDOI
An ensemble approach for data fusion with learn
Michael Lewitt,Robi Polikar +1 more
TL;DR: This paper presents Learn++ as an addition to the new breed of classifier fusion algorithms, along with preliminary results obtained on two real-world data fusion applications.
Proceedings ArticleDOI
Off-line handwritten Chinese character recognition based on crossing line feature
TL;DR: A new method to extract crossing line features for off-line handwritten Chinese character recognition is proposed, in which the input pattern is nonlinearly normalized in order to compensate for shape variations.
Journal ArticleDOI
Flexible nonlinear contextual classification
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Automatic Detection of Expanding H I Shells in the Canadian Galactic Plane Survey Data
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DissertationDOI
Multiple classifier systems incorporating uncertainty
TL;DR: The inclusion of uncertain class information into multi classifier systems (MCS) is the central theme in this thesis and formal uncertainty theories are assessed regarding their aptitude to support the core flavours of uncertainty in MCS: vagueness, imprecision, and certainty.
References
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Journal ArticleDOI
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