Exploiting Reliability for Dynamic Selection of Classifiers by Means of Genetic Algorithms
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Methods and algorithms of collective recognition
Automation and Remote Control
Classifier combination based on confidence transformation
Pattern Recognition
A PSO-based weighting method for linear combination of neural networks
Computers and Electrical Engineering
Neuro-fuzzy-combiner: an effective multiple classifier system
International Journal of Knowledge Engineering and Soft Data Paradigms
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The paper examines the general classifier combination problem under strict separation of the classifier and combinator design. Several desirable combinator properties are identified: omnitype mixed type and correlated classifier combination, redundant classifier elimination, model complexity control, and dynamic selection combination. By adapting some of the theories and algorithms developed for neural network learning. They present a combination model which provides a solution to these problems. Experimental results on handwritten digits verify these findings.