On Distributed Online Classification in the Midst of Concept Drifts
arXiv:1301.0047 · doi:10.1016/j.neucom.2012.12.043
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.
19 pages, 14 figures, to appear in Neurocomputing, 2013