Harnessing heterogeneity in space with statistically guided meta-learning

Spatial data are ubiquitous, massively collected, and widely used to support critical decision-making in many societal domains, including public health (e.g., COVID-19 pandemic control), agricultural crop monitoring, transportation, etc. While recent advances in machine learning and deep learning of...

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Detalles Bibliográficos
Autores principales: Xie, Yiqun, Chen, Weiye, He, Erhu, Jia, Xiaowei, Bao, Han, Zhou, Xun, Ghosh, Rahul, Ravirathinam, Praveen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer London 2023
Materias:
Regular Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9994417/
https://www.ncbi.nlm.nih.gov/pubmed/37035130
http://dx.doi.org/10.1007/s10115-023-01847-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9994417/
https://www.ncbi.nlm.nih.gov/pubmed/37035130
http://dx.doi.org/10.1007/s10115-023-01847-0

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