1 research outputs found
Classification in a Large Network
We construct and analyze the communication cost of protocols (interactive and
one-way) for classifying , in a network with nodes, with known only at node
. The classifier takes the form , with
weights . The interactive protocol (a zero-error protocol)
exchanges a variable number of messages depending on the input
and its sum rate is directly proportional to its mean stopping time. An exact
analysis, as well as an approximation of the mean stopping time is presented
and shows that it depends on , where
and , with being the number of positive weights. In particular,
the mean stopping time grows logarithmically in when , and is
bounded in otherwise. Comparisons show that the sum rate of the interactive
protocol is smaller than that of the one-way protocol when the error
probability for the one-way protocol is small, with the reverse being true when
the error probability is large. Comparisons of the interactive protocol are
also made with lower bounds on the sum rate.Comment: 5 page