Support Vector Machines, Data Reduction, and Approximate Kernel Matrices
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
A support vector machine with integer parameters
Neurocomputing
Optimal sensor selection in binary heterogeneous sensor networks
IEEE Transactions on Signal Processing
A collaborative training algorithm for distributed learning
IEEE Transactions on Information Theory
A decentralized approach for nonlinear prediction of time series data in sensor networks
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
Practical data compression in wireless sensor networks: A survey
Journal of Network and Computer Applications
Decentralized Estimation using distortion sensitive learning vector quantization
Pattern Recognition Letters
Wireless Personal Communications: An International Journal
Hi-index | 35.75 |
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.