Title: Decentralized Detection and Classification using Kernel Methods
Author: XuanLong Nguyen, Martin J. Wainwright and Michael I. Jordan
Date: April 2004
Pub: PDF
Url: http://www.stat.berkeley.edu/~jordan/658.pdf

Abstract:
We consider the problem of decentralized detection under constraints
on the number of bits that can be transmitted by each sensor.  In
contrast to most previous work, in which the joint distribution of
sensor observations is assumed to be known, we address the problem
when only a set of empirical samples is available.  We propose a novel
algorithm using the framework of empirical risk minimization and
marginalized kernels, and analyze its computational and statistical
properties both theoretically and empirically.  We provide an
efficient implementation of the algorithm, and demonstrate its
performance on both simulated and real data sets.