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Abstract:
In this short note, building on ideas of M. Herbster [2] we propose a method for automatically tuning the
parameter of the FIXED-SHARE algorithm proposed by Herbster and
Warmuth [3] in the context of on-line learning with
shifting experts. We show that this can be done with a memory
requirement of O(nT) and that the additional loss incurred by
the tuning is the same as the loss incurred for estimating the
parameter of a Bernoulli random variable.
