2018
Boloix-Tortosa, Rafael; Murillo-Fuentes, Juan José; Payán-Somet, Francisco Javier; Pérez-Cruz, Fernando
Complex Gaussian Processes for Regression Artículo de revista
En: IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no 11, pp. 5499-5511, 2018.
Enlaces | BibTeX | Etiquetas: Complex-valued processes, Gaussian processes (GPs), kernel methods, regression
@article{FPerez18,
title = {Complex Gaussian Processes for Regression},
author = {Rafael Boloix-Tortosa and Juan Jos\'{e} Murillo-Fuentes and Francisco Javier Pay\'{a}n-Somet and Fernando P\'{e}rez-Cruz},
doi = {10.1109/TNNLS.2018.2805019},
year = {2018},
date = {2018-03-06},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
volume = {29},
number = {11},
pages = {5499-5511},
keywords = {Complex-valued processes, Gaussian processes (GPs), kernel methods, regression},
pubstate = {published},
tppubtype = {article}
}
2010
Perez-Cruz, Fernando; Kulkarni, S R
Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks Artículo de revista
En: IEEE Signal Processing Letters, vol. 17, no 4, pp. 355–358, 2010, ISSN: 1070-9908.
Resumen | Enlaces | BibTeX | Etiquetas: communication complexity, Consensus, distributed learning, kernel methods, learning (artificial intelligence), low complexity distributed kernel least squares le, message passing, message-passing algorithms, robust nonparametric statistics, sensor network learning, sensor networks, telecommunication computing, Wireless Sensor Networks
@article{Perez-Cruz2010,
title = {Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks},
author = {Fernando Perez-Cruz and S R Kulkarni},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5395679},
issn = {1070-9908},
year = {2010},
date = {2010-01-01},
journal = {IEEE Signal Processing Letters},
volume = {17},
number = {4},
pages = {355--358},
abstract = {We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for sensor network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.},
keywords = {communication complexity, Consensus, distributed learning, kernel methods, learning (artificial intelligence), low complexity distributed kernel least squares le, message passing, message-passing algorithms, robust nonparametric statistics, sensor network learning, sensor networks, telecommunication computing, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {article}
}