I.e. AdaBoost provides the list of partial hypotheses and weights for them. But at some point it tends to start repeating the same hypotheses over and over again, because a set of hypotheses taken together gives some gain, and applying it multiple times seemingly increases this gain. This gain is subject to the same problems as the Bayesian computations with the duplicate events: it increases the confidence only seemingly. So the same solution can be applied to it:

After the set of partial AdaBoost hypotheses has been computed, they can be re-ordered in such a way as to remove the duplication, by selecting on every step the partial hypotheses with the

*smallest*effect. This "run order" can be kept and updated on every step, instead of the AdaBoost's "selection order". So when the sequence will start repeating, a duplicate hypothesis would add nothing to the cumulative effect of the "run order", and at this point the selection algorithm should try a different hypothesis (or simply stop).

I guess, in case if the selection algorithm doesn't stop but tries to do the next best pick (and then maybe another one if the second choice is no good either), it becomes fundamentally more complicated: on each iteration of AdaBoost it should select not simply the hypotheses that maximizes the effect of the whole sequence when appended to the end of it but one that maximizes the effect of the whole sequence when inserted at the position in the sequence where it adds the least effect. Each such insert could also cause a re-ordering of the part of the sequence that follows it.

Which sounds even more heavy-weight than the original AdaBoost selection of the hypothesis. On the other hand, since the AdaBoost selection is already so heavy-weight, maybe this would make little extra overhead. I don't know how to analyze it yet.

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