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We study learning of initial intervals in the prediction model. We show that for each distribution D over the domain, there is an algorithm AD, whose probability of a mistake in round m is at most ( + o(1))/m. We also show that the best possible bound that can be achieved in the case in which the same algorithm A must be applied for all distributions D is at least (1??e - o(1))1?m > (3?5-o(1))1?m. Informally, "knowing" the distribution D enables an algorithm to reduce its error rate by a constant factor strictly greater than 1. As advocated by Ben-David et al. (2008), knowledge of D can be viewed as an idealized proxy for a large number of unlabeled examples.

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