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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Dissertation
The construction and evaluation of statistical models of melodic structure in music perception and composition
TL;DR: The thesis proposed in this dissertation is that statistical models which acquire knowledge through the induction of regularities in corpora of existing music can, if examined with appropriate methodologies, provide significant insights into the cognitive processing involved in music perception and composition.
Proceedings Article
Bayesian Classifier Combination
Hyun-Chul Kim,Zoubin Ghahramani +1 more
TL;DR: Bayesian model averaging as discussed by the authors is the coherent Bayesian way of combining multiple models only under certain restrictive assumptions, which is the framework for Bayesian model combination (which differs from model averaging) in the context of classification.
Journal ArticleDOI
Random Sampling for Subspace Face Recognition
Xiaogang Wang,Xiaoou Tang +1 more
TL;DR: An ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters is developed, and a robust random sampling face recognition system integrating shape, texture, and Gabor responses is constructed.
Journal ArticleDOI
Fusion of multiple classifiers for intrusion detection in computer networks
TL;DR: A pattern recognition approach to network intrusion detection based on the fusion of multiple classifiers is proposed, and five decision fusion methods are assessed by experiments and their performances compared.
References
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