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For classifier ensembles, an effective combination method is to combine the outputs of each classifier using a linearly weighted combination rule. There are multiple ways to linearly combine classifier outputs and it is beneficial to analyze them as a whole. We present a unifying framework for multiple linear combination types in this paper. This unification enables using the same learning algorithms...
Classifier combination is an important research area since they have a significant contribution to the accuracy. Even though simple fixed combination rules result in satisfactory performances, supervised combination learning surely has higher probability of better accuracy. Among supervised combination methods, linear combiner is one of the well-known methods. In this work, different types of linear...
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