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On Distributed Online Classification in the Midst of Concept Drifts (1301.0047v1)
Published 1 Jan 2013 in math.OC, cs.DC, cs.LG, cs.SI, and physics.soc-ph
Abstract: In this work, we analyze the generalization ability of distributed online learning algorithms under stationary and non-stationary environments. We derive bounds for the excess-risk attained by each node in a connected network of learners and study the performance advantage that diffusion strategies have over individual non-cooperative processing. We conduct extensive simulations to illustrate the results.
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