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A scalable support vector machine (SVM) is proposed for distributed classification in ad hoc wireless sensor networks (WSNs) in this paper. The main idea is to train SVM classifier using only the local dataset, and evaluate the global nonlinear classifier via a dynamic consensus algorithm with communication only between neighbors instead of among all agents (sensor node) in the network. Specifically,...
A totally distributed and scalable support vector machine (DSVM) for classification in ad hoc wireless sensor networks (WSNs) is proposed. A sequential gradient ascent based algorithm is first introduced and adapted for distributed and parallel SVM training using only the local dataset for each classification agent. Then the global nonlinear classifier is evaluated via a dynamic consensus algorithm...
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