Emergent Mind
On Distributed Online Classification in the Midst of Concept Drifts
(1301.0047)
Published Jan 1, 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.
We're not able to analyze this paper right now due to high demand.
Please check back later (sorry!).
Generate a summary of this paper on our Pro plan:
We ran into a problem analyzing this paper.