Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Marié, Sylvain"'
Autor:
Zheng, Zhiyu a, b, ⁎, Marié, Sylvain d, ⁎, Farazdaghi, Elham a, Yahia, Esma c, Makhoul, Khal d, Lagarde, Théo d, Meouche, Rani El a, ⁎, Ababsa, Fakhreddine b
Publikováno v:
In Energy & Buildings 1 February 2025 328
Akademický článek
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Publikováno v:
In Information Sciences December 2017 418-419:272-285
Autor:
Albarede, Lucas1,2 (AUTHOR) lucas.albarede@protonmail.com, Mulhem, Philippe1 (AUTHOR), Goeuriot, Lorraine1 (AUTHOR), Marié, Sylvain2 (AUTHOR), Le Pape-Gardeux, Claude2 (AUTHOR), Chardin-Segui, Trinidad2 (AUTHOR)
Publikováno v:
Information Retrieval Journal. Dec2023, Vol. 26 Issue 1/2, p1-25. 25p.
Publikováno v:
ECML-PKDD 2022-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
ECML-PKDD 2022-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2022, Grenoble, France. pp.1-16
ECML-PKDD 2022-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2022, Grenoble, France. pp.1-16
International audience; Learning the structure of Bayesian networks from data is a NP-Hard problem that involves optimization over a super-exponential sized space. Still, in many real-life datasets a number of the arcs contained in the final structur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______165::24bb505197fe742100f3582be98494f2
https://hal.science/hal-03873684v2/document
https://hal.science/hal-03873684v2/document
Autor:
Gao Tianyun, Boguslawski Bartosz, Marié Sylvain, Béguery Patrick, Thebault Simon, Lecoeuche Stéphane
Publikováno v:
E3S Web of Conferences, Vol 111, p 05009 (2019)
Data-driven automatic fault detection and diagnostics (AFDD) have gained a lot of research attention in recent years. Many existing solutions need to learn from the fault operation data to be able to diagnose the faults. However, these data are usual
Externí odkaz:
https://doaj.org/article/bd37e7f82cae42ac89691f8e9f6dc35c
Autor:
Marié, Sylvain
Regression trees are powerful Machine Learning models capable of both flexibility in modeling as well as interpretability when the tree is not too deep. The M5 algorithm was introduced by Quinlan in 1992 under the name "model tree" ; the algorithm is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______166::5793cfe8cc83aaccf1110dc4488e9821
https://hal.archives-ouvertes.fr/hal-03762155
https://hal.archives-ouvertes.fr/hal-03762155
Autor:
Chevalier, Marcel, Marié, Sylvain, Boguslawski, Bartosz, Cercueil, Michel, Chupot, François, Vignon, Antoine, Youssef, Wedian
Publikováno v:
CIGI QUALITA 2021
CIGI QUALITA 2021, May 2021, Grenoble, France
CIGI QUALITA 2021, May 2021, Grenoble, France
Industrial end users face an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric we tackle those two issues using b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::b85a757f608d2294a6ee06927892c0a2
https://hal.archives-ouvertes.fr/hal-03320965
https://hal.archives-ouvertes.fr/hal-03320965
Autor:
Gouin, Victor, Alvarez-Hérault, Marie-Cécile, Deschamps, Philippe, Marié, Sylvain, Lamoudi, Yacine
Publikováno v:
France, Patent n° : . 2016
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::f76b4ca354aa25e1663d13825752b9ec
https://hal.archives-ouvertes.fr/hal-01518688
https://hal.archives-ouvertes.fr/hal-01518688
Publikováno v:
Actes de la conférence RFIA 2012
RFIA 2012 (Reconnaissance des Formes et Intelligence Artificielle)
RFIA 2012 (Reconnaissance des Formes et Intelligence Artificielle), Jan 2012, Lyon, France. pp.978-2-9539515-2-3
HAL
RFIA 2012 (Reconnaissance des Formes et Intelligence Artificielle)
RFIA 2012 (Reconnaissance des Formes et Intelligence Artificielle), Jan 2012, Lyon, France. pp.978-2-9539515-2-3
HAL
Session "Posters"; National audience; Cet article propose de comparer différentes métriques de séries temporelles, afin de suggérer les méthodes les plus adaptées pour l'analyse de données énergétiques du bâtiment. Dans un premier temps, la
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::83e33d8f80caa014019bf67d4d244787
https://hal.archives-ouvertes.fr/hal-00656546
https://hal.archives-ouvertes.fr/hal-00656546