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Communication Dans Un Congrès Année : 2009

Functional estimation in Hilbert space for distributed learning in wireless sensor networks

Résumé

In this paper, we propose a distributed learning strategy in wireless sensor networks. Taking advantage of recent developments on kernel-based machine learning, we consider a new sparsification criterion for online learning. As opposed to previously derived criteria, it is based on the estimated error and is therefore is well suited for tracking the evolution of systems over time. We also derive a gradient descent algorithm, and we demonstrate its relevance to estimate the dynamic evolution of temperature in a given region.
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Dates et versions

hal-01966048 , version 1 (27-12-2018)

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Paul Honeine, Cédric Richard, José C. M. Bermudez, Hichem Snoussi, Mehdi Essoloh, et al.. Functional estimation in Hilbert space for distributed learning in wireless sensor networks. Proc. 34th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2009, Taipei, Taiwan. pp.2861-2864, ⟨10.1109/ICASSP.2009.4960220⟩. ⟨hal-01966048⟩
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