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- W1978111811 abstract "In this paper, we study distributed classification of targets in a large scale sensor network setting. Specifically, we consider sensor nodeswhich canmeasure only a part of the feature vec- tor and whose communication capabilities are limited to only their neighbouring nodes. We formulate a distributed classifi- cation algorithm that learns the optimal (large-margin) hyper- plane separating the two classes, using the projected-gradient approach. The sensor nodes reach a consensus on the gradient to be used for weight update at each step of the optimization algorithm. We prove the convergence of the proposed algorithm and provide a joint calibration and signature learning method for acoustic sensor networks as an application." @default.
- W1978111811 created "2016-06-24" @default.
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- W1978111811 date "2009-08-01" @default.
- W1978111811 modified "2023-09-25" @default.
- W1978111811 title "Distributed hyperplane learning using consensus algorithm for sensor networks" @default.
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- W1978111811 doi "https://doi.org/10.1109/ssp.2009.5278461" @default.
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