Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks

This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal Gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covarian...

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Detalles Bibliográficos
Autores principales: Xu, Yunfei, Choi, Jongeun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2011
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231612/
https://www.ncbi.nlm.nih.gov/pubmed/22163785
http://dx.doi.org/10.3390/s110303051

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231612/
https://www.ncbi.nlm.nih.gov/pubmed/22163785
http://dx.doi.org/10.3390/s110303051

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