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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Teacher-directed learning in view-independent face recognition with mixture of experts using overlapping eigenspaces
TL;DR: The experimental results support the claim that directing the experts to a predetermined partitioning of the face space improves the performance of the conventional ME for view-independent face recognition.
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A new classifier fusion method based on historical and on-line classification reliability for recognizing common CT imaging signs of lung diseases
TL;DR: The proposed classifier fusion method is applied to combine five types of classifiers for CISL recognition, including support vector machine (SVM), back-propagation neural network (BPNN), Naïve Bayes (NB), k-nearest neighbor (k-NN) and decision tree (DT).
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Leveraging the Strengths of Choice Models and Neural Networks: A Multiproduct Comparative Analysis*
TL;DR: The results are particularly important in brand management and customer relationship management, indicating that multiple technologies and mixture of technologies may yield more accurate and reliable outcomes than individual ones.
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Automatic disruption classification at JET: comparison of different pattern recognition techniques
Barbara Cannas,Francesca Cau,Alessandra Fanni,Piergiorgio Sonato,Maria Katiuscia Zedda,Jet-Efda Contributors +5 more
TL;DR: Multi-layer perceptron classifiers exhibited the best performance in classifying mode lock, density limit/high radiated power, H-mode/L-mode transition and internal transport barrier plasma disruptions, and can be increased using multiple classifiers.
Journal ArticleDOI
Unified decision combination framework
TL;DR: A unified framework for decision combination is presented and a new parameterized combination method (pooled ranking figure of merit) is presented which is shown to be equivalent to three of the standard combination methods.
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