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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Exploration of classification confidence in ensemble learning
TL;DR: This work extends the definition of ensemble margin based on the classification confidence of the base classifiers and compares the proposed fusion technique with some classical algorithms to show the effectiveness of weighted voting with classification confidence.
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Classification of Arabic script using multiple sources of information: State of the art and perspectives
TL;DR: It is shown that in order to improve classification results obtained with single classifiers, it is necessary to combine several sources of information either at the level of feature extraction/description, or at the classification stage, orat both levels.
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Vehicle Reidentification using multidetector fusion
TL;DR: An investigation into the feasibility of fusing inductive vehicle signatures with video for anonymous vehicle reidentification shows that this approach merits further investigation and provides system redundancy and yields slightly better results than the use of a single detector.
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Multiple classifiers combination by clustering and selection
Rujie Liu,Baozong Yuan +1 more
TL;DR: The performance comparison between M3CS and Kuncheva's CS+DT method, as well as some simple aggregation methods such as maximum, minimum, average, and majority vote, confirms the validity of the proposed scheme.
BookDOI
Machine Learning in Document Analysis and Recognition
TL;DR: The main goals of the book are identification of good practices for the use of learning strategies in DAR, identification of DAR tasks more appropriate for these techniques, and highlighting new learning algorithms that may be successfully applied to DAR.
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