  This paper presents an adaptive combination strategy for distributed learning
over diffusion networks. Since learning relies on the collaborative processing
of the stochastic information at the dispersed agents, the overall performance
can be improved by designing combination policies that adjust the weights
according to the quality of the data. Such policies are important because they
would add a new degree of freedom and endow multi-agent systems with the
ability to control the flow of information over their edges for enhanced
performance. Most adaptive and static policies available in the literature
optimize certain performance metrics related to steady-state behavior, to the
detriment of transient behavior. In contrast, we develop an adaptive
combination rule that aims at optimizing the transient learning performance,
while maintaining the enhanced steady-state performance obtained using policies
previously developed in the literature.
