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Literatura académica sobre el tema "Algorithme en ligne avec conseil"
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Artículos de revistas sobre el tema "Algorithme en ligne avec conseil"
ADELINE-DUFLOT, F., E. FERRAT, S. BROSSIER, R. GAUZIT, R. COHEN y V. RENARD. "Conseil téléphonique en prescription antibiotique (CoTéPrAgmatique) auprès des médecins généralistes : résultats de deux ans de fonctionnement". EXERCER 32, n.º 176 (1 de octubre de 2021): 352–58. http://dx.doi.org/10.56746/exercer.2021.176.352.
Texto completoClarke, Graham. "Décisions rendues par le Conseil canadien des relations du travail". Relations industrielles 44, n.º 4 (12 de abril de 2005): 933–46. http://dx.doi.org/10.7202/050543ar.
Texto completoOh, Jiwon, Marie-Sarah Gagné-Brosseau, Melanie Guenette, Catherine Larochelle, François Lemieux, Suresh Menon, Sarah A. Morrow et al. "Toward a Shared-Care Model of Relapsing-Remitting Multiple Sclerosis: Role of the Primary Care Practitioner". Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 45, n.º 3 (mayo de 2018): 304–12. http://dx.doi.org/10.1017/cjn.2018.7.
Texto completoDouzon-Bernal, M. "Un dispositif global de soins des troubles de la relation mère–nourrisson". European Psychiatry 28, S2 (noviembre de 2013): 57. http://dx.doi.org/10.1016/j.eurpsy.2013.09.149.
Texto completoSolère, Jean-Luc. "Bayle historien et critique du matérialisme dans le dictionnaire". Kriterion: Revista de Filosofia 50, n.º 120 (diciembre de 2009): 423–36. http://dx.doi.org/10.1590/s0100-512x2009000200010.
Texto completoPéguret, Muriel y Catherine Lamaison. "L’influence du Cadre européen commun de référence pour les langues (2001) sur l’enseignement du français langue seconde au niveau universitaire au Canada". La Revue de l’AQEFLS 33, n.º 1 (16 de septiembre de 2021): 35–44. http://dx.doi.org/10.7202/1081268ar.
Texto completoChauvard, Jean-François. "Adaptabilité versus inaliénabilité Les dérogations des fidéicommis dans la Venise du XVIIIe siècle". Annales. Histoire, Sciences Sociales 70, n.º 04 (diciembre de 2015): 849–78. http://dx.doi.org/10.1353/ahs.2015.0199.
Texto completoNaji, Khadija y Abdelali Ibriz. "Approach for Eliciting Learners' Preferences in Moocs Through Collaborative Filtering". International Journal of Emerging Technologies in Learning (iJET) 17, n.º 14 (26 de julio de 2022): 235–45. http://dx.doi.org/10.3991/ijet.v17i14.29887.
Texto completoALLEREAU, P., E. ALLORY y A. MAURY. "Les avis en ligne concernant les MG. Une étude par analyse de contenu en Ille-et-Vilaine". EXERCER 35, n.º 200 (1 de febrero de 2024): 64–69. http://dx.doi.org/10.56746/exercer.2024.200.64.
Texto completoVeall, Michael R. "Internationalization and the Internet: The Case of Canadian Public Policy/Analyse de politiques". Scholarly and Research Communication 11, n.º 1 (10 de marzo de 2020): 9. http://dx.doi.org/10.22230/src.2020v11n1a351.
Texto completoTesis sobre el tema "Algorithme en ligne avec conseil"
Jin, Shendan. "Online computation beyond standard models". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS152.
Texto completoIn the standard setting of online computation, the input is not entirely available from the beginning, but is revealed incrementally, piece by piece, as a sequence of requests. Whenever a request arrives, the online algorithm has to make immediately irrevocable decisions to serve the request, without knowledge on the future requests. Usually, the standard framework to evaluate the performance of online algorithms is competitive analysis, which compares the worst-case performance of an online algorithm to an offline optimal solution. In this thesis, we will study some new ways of looking at online problems. First, we study the online computation in the recourse model, in which the irrevocability on online decisions is relaxed. In other words, the online algorithm is allowed to go back and change previously made decisions. More precisely, we show how to identify the trade-off between the number of re-optimization and the performance of online algorithms for the online maximum matching problem. Moreover, we study measures other than competitive analysis for evaluating the performance of online algorithms. We observe that sometimes, competitive analysis cannot distinguish the performance of different algorithms due to the worst-case nature of the competitive ratio. We demonstrate that a similar situation arises in the linear search problem. More precisely, we revisit the linear search problem and introduce a measure, which can be applied as a refinement of the competitive ratio. Last, we study the online computation in the advice model, in which the algorithm receives as input not only a sequence of requests, but also some advice on the request sequence. Specifically, we study a recent model with untrusted advice, in which the advice can be either trusted or untrusted. Assume that in the latter case, the advice can be generated from a malicious source. We show how to identify a Pareto optimal strategy for the online bidding problem in the untrusted advice model