Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Same, Allou"'
The Covid-19 pandemic drastically changed urban mobility, both during the height of the pandemic with government lockdowns, but also in the longer term with the adoption of working-from-home policies. To understand its effects on rail public transpor
Externí odkaz:
http://arxiv.org/abs/2402.12392
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
In Neurocomputing 21 August 2022 500:217-230
Autor:
Leyli-Abadi, Milad, Samé, Allou, Oukhellou, Latifa, Cheifetz, Nicolas, Mandel, Pierre, Féliers, Cédric, Heim, Véronique
Publikováno v:
In Neurocomputing 7 June 2021 439:176-196
Autor:
Heinrich, Matthias, Meunier, Simon, Samé, Allou, Quéval, Loïc, Darga, Arouna, Oukhellou, Latifa, Multon, Bernard
Publikováno v:
In Applied Energy 1 April 2020 263
This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governed by hidden Markov chains. The underlying hidden process for each clus
Externí odkaz:
http://arxiv.org/abs/1312.7024
This paper proposes a method of segmenting temporal data into ordered classes. It is based on mixture models and a discrete latent process, which enables to successively activates the classes. The classification can be performed by maximizing the lik
Externí odkaz:
http://arxiv.org/abs/1312.7011
A new approach for feature extraction from time series is proposed in this paper. This approach consists of a specific regression model incorporating a discrete hidden logistic process. The model parameters are estimated by the maximum likelihood met
Externí odkaz:
http://arxiv.org/abs/1312.7001
A new approach for signal parametrization, which consists of a specific regression model incorporating a discrete hidden logistic process, is proposed. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expec
Externí odkaz:
http://arxiv.org/abs/1312.6994