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.
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