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
Design and Fusion of Pose-Invariant Face-Identification Experts
Tae-Kyun Kim,Josef Kittler +1 more
TL;DR: The proposed fusion architecture of the pose-invariant face experts achieves an impressive accuracy gain by virtue of the individual experts diversity.
Journal ArticleDOI
A Bayesian framework for the combination of classifier outputs
TL;DR: A sequential Bayesian framework to estimate the posterior probability of being in a certain class given multiple classifiers is proposed, which employs meta-Gaussian modelling but makes no assumptions about the distribution of classifier outputs.
Journal ArticleDOI
Algorithmic Fusion for More Robust Feature Tracking
TL;DR: A framework for merging the results of independent feature-based motion trackers using a classification based approach is presented and how synthetic data can be used effectively to overcome the major problem with such systems is generating ground truth data for training is shown.
Proceedings Article
Artificial neural network fusion: Application to Arabic words recognition.
TL;DR: Two types of features for handwritten Arabic literal words amount recognition, using neural network classiflers are discussed and their results compared with a single classifler benchmark using a complete feature set are presented.
Proceedings ArticleDOI
Class-wise multi-classifier combination based on Dempster-Shafer theory
Bin Zhang,Sargur N. Srihari +1 more
TL;DR: In this paper, a multi-classifier combination based on Dempster-Shafer theory of evidence has demonstrated superior performance and outperformed the traditional one based on classifiers' global performances.
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