Focusing on classification problems, this paper presents a new method for linearly combining discriminant-based classifiers to improve classification performance, in the sense of the minimum classification errors. In our approach, the problem of estimating linear weights in combination is reformulated as the problem of designing a linear discriminant function using the minimum classification error discriminant. In this formulation, because the classification decision rule is incorporated into the cost function, better combination weights suitable for the classification objective can be obtained. Experimental results using neural network classifiers support the effectiveness of the proposed method.
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