Honeine, Paul and Richard, Cédric and Bermudez, José Carlos M. and Snoussi, Hichem and Essoloh, Mehdi and Vincent, François Functional estimation in Hilbert space for distributed learning in wireless sensor network. (2009) In: IEEE International conference on acoustics, speech and signal processing, 19-24 April 2009, Taipei, Taiwan.
|(Document in English) |
PDF (Author's version) - Depositor and staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
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 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.
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||This paper appears in : IEEE International conference on acoustics, speech and signal processing, 19 - 24 April 2009, Taipei International Convention Center, Taiwan.-ISBN 978-1-424-42353-8|
|Audience (conference):||International conference proceedings|
|Institution:||Other partners > Université de Technologie de Troyes - UTT (FRANCE)|
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE
Other partners > Federal University of Santa Catarina - UFSC (BRAZIL)
Institut Charles Delaunay - ICD (Troyes, France)
Department of Electrical Engineering (Florianopolis, Brazil)
Département d'Electronique, Optronique et Signal - DEOS (Toulouse, France) - Signal, Communication, Antenne et Navigation - SCAN
|Deposited By:||Francois VINCENT|
Repository Staff Only: item control page