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

Functional estimation in Hilbert space for distributed learning in wireless sensor networks. Honeine, P., Richard, C., Bermudez, J. C. M., Snoussi, H., Essoloh, M., & Vincent, F. In Proc. 34th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2861-2864, Taipei, Taiwan, April, 2009.

Link

Functional estimation in Hilbert space for distributed learning in wireless sensor networks [pdf]

Paper doi abstract bibtex

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.

@INPROCEEDINGS{Hon09.icassp,
   author =  "Paul Honeine and Cédric Richard and José C. M. Bermudez and Hichem Snoussi and Mehdi Essoloh and François Vincent",
   title =  "Functional estimation in Hilbert space for distributed learning in wireless sensor networks",
   booktitle =  "Proc. 34th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
   address =  "Taipei, Taiwan",
   month = apr,
   year  =  "2009",
   pages={2861-2864}, 
   doi={10.1109/ICASSP.2009.4960220}, 
   ISSN={1520-6149},
   keywords  =  "machine learning, sparsity, adaptive filtering, wireless sensor networks",
   url_link= "https://ieeexplore.ieee.org/document/4960220",
   url_paper   =  "http://honeine.fr/paul/publi/09.icassp.pdf",
   acronym =  "ICASSP",
   abstract={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.}, 
   keywords={distributed algorithms, Hilbert spaces, intelligent sensors, learning (artificial intelligence), nonlinear systems, wireless sensor networks, functional estimation, Hilbert space, distributed learning, wireless sensor networks, machine learning, intelligent sensors, adaptive estimation, nonlinear systems, Hilbert space, Wireless sensor networks, Kernel, Temperature sensors, Acoustic sensors, Sensor phenomena and characterization, Machine learning, Intelligent sensors, Intelligent networks, Space technology, Intelligent sensors, adaptive estimation, distributed algorithms, nonlinear systems}, 
}

Downloads: 0