StrCombo: combination of string recognizers
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Multiple Classifier Combination Methodologies for Different Output Levels
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A New Evaluation Method for Expert Combination in Multi-expert System Designing
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Bank-check Processing System: Modifications Due to the New European Currency
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
A New Classifier Simulator for Evaluating Parallel Combination Methods
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Evaluation of the Information-Theoretic Construction of Multiple Classifier Systems
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Improving a bank-check processing system with new HMM-based algorithms
AIC'05 Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications
Simulating classifier outputs for evaluating parallel combination methods
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
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This paper presents a new approach to evaluate the performances of combination methods, which takes into account both the recognition rates of the experts combined and the correlation among them. At the purpose, a suitable estimator of correlation is defined. Two combination methods have been considered: Majority Vote and Dempster Shafer. A statistical test, based on the analysis of variance, has also been used to infer some interesting considerations on the behavior of combination methods. The paper shows how the proposed approach allows the selection of the best combination method for each set of experts.