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pro vyhledávání: '"Catharina Elisabeth Graafland"'
In light of the ongoing global climate change, a better understanding of global wildfire activity is the key to anticipate future impacts and minimize, as much as possible, their negative consequences on natural ecosystems and human economy. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe996ebea74015e5a73cb67bdcc7833d
https://doi.org/10.5194/egusphere-egu23-9608
https://doi.org/10.5194/egusphere-egu23-9608
Autor:
Diego Pazó, José M. Gutiérrez, Miguel A. Rodríguez, Catharina Elisabeth Graafland, Juan M. López
Publikováno v:
Scientific Reports, Vol 10, Iss 1, Pp 1-15 (2020)
Scientific Reports
Digital.CSIC. Repositorio Institucional del CSIC
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Scientific Reports
Digital.CSIC. Repositorio Institucional del CSIC
instname
Complex systems often exhibit long-range correlations so that typical observables show statistical dependence across long distances. These teleconnections have a tremendous impact on the dynamics as they provide channels for information transport acr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b7a65c24d8e217e45c51fb9a0309538
http://link.springer.com/article/10.1038/s41598-020-67970-y
http://link.springer.com/article/10.1038/s41598-020-67970-y
Probabilistic network models (PNMs) are well established data-driven modeling and machine learning prediction techniques used in many disciplines, including climate analysis. These techniques can efficiently learn the underlying (spatial) dependency
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b8a1fe4533be080408c72b94485a44f6
https://doi.org/10.5194/egusphere-egu22-12822
https://doi.org/10.5194/egusphere-egu22-12822
Publikováno v:
Digital.CSIC. Repositorio Institucional del CSIC
instname
instname
Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence tests to learn the dependence structure of the data; score-based algorithm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::422929491abc1fb9a534ff273bba4124
http://hdl.handle.net/10261/213670
http://hdl.handle.net/10261/213670
Publikováno v:
ACM Transactions on Knowledge Discovery from Data; Mar2022, Vol. 16 Issue 5, p1-25, 25p
Autor:
Gracia Borobia, Sergio
Trabajo fin de Máster defendido en la Facultad de Ciencias de la Universidad de Cantabria, el 15 de julio de 2022 -Curso 2021-2022 - Máster Interuniversitario en Ciencia de Datos / Master in Data Science (UIMP-UC-CSIC)
[EN] In light of the ong
[EN] In light of the ong
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1106::284f5ba167446b55cbe8f7d23a31d2dc
http://hdl.handle.net/10261/280877
http://hdl.handle.net/10261/280877
This graduate-level textbook is ideally suited for lecturing the most relevant topics of Edge Computing and its ties to Artificial Intelligence (AI) and Machine Learning (ML) approaches. It starts from basics and gradually advances, step-by-step, to
This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning al