Online Learning of Non-stationary Sequences

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Online Learning of Non-stationary Sequences
We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. We derive upper and lower relative loss bounds for a class of universal learning algorithms involving a switching dynamics over the choice of the experts. On the basis of the performance bounds we provide the optimal a priori discretization for learning the parameter that governs the switching dynamics. We demonstrate the new algorithm in the context of wireless networks.
Claire Monteleoni, Tommi Jaakkola
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NIPS
Authors Claire Monteleoni, Tommi Jaakkola
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