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I have a single classifier which produces probability vectors (a probability for each category). I know ahead of time that certain sets of input samples should result in the same, unknown label. With no other knowledge, how should I choose the "correct" label for these samples when I get conflicting predictions from each one? A friend suggested I take the geometric mean of the probability vectors, if that's the case can anyone explain the motivation behind this?

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