Online Learning over Graphs
Mark Herbster, Massimiliano Pontil and Lisa Wainer
In: ICML 2005, 7-11 August 2005, Bonn, Germany.
We apply classic online learning techniques
similar to the perceptron algorithm to the
problem of learning a function defined on a
graph. The benefit of our approach includes
simple algorithms and performance guaran-
tees that we naturally interpret in terms of
structural properties of the graph, such as the
algebraic connectivity or the diameter of the
graph. We also discuss how these methods
can be modified to allow active learning on a
graph. We present preliminary experiments
with encouraging results.