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ICML
2004
IEEE

Decentralized detection and classification using kernel methods

14 years 4 months ago
Decentralized detection and classification using kernel methods
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.
XuanLong Nguyen, Martin J. Wainwright, Michael I.
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2004
Where ICML
Authors XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
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