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A theoretical study on six classifier fusion strategies

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1 Author(s)
Kuncheva, L.I. ; Sch. of Informatics, Univ. of Wales, Bangor, UK

We look at a single point in feature space, two classes, and L classifiers estimating the posterior probability for class ω1 . Assuming that the estimates are independent and identically distributed (normal or uniform), we give formulas for the classification error for the following fusion methods: average, minimum, maximum, median, majority vote, and oracle

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:24 ,  Issue: 2 )