Academic literature on the topic 'Détection des Dérives Conceptuelles'
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Journal articles on the topic "Détection des Dérives Conceptuelles":
Laurin, Suzanne, Luc Côté, Lise Cusson, Leyla Korany, Annie Jolicoeur, and Nicolas Fernandez. "Les facteurs et enjeux relationnels lors de l’évaluation des résidents en médecine familiale." Pédagogie Médicale 23, no. 1 (2022): 7–16. http://dx.doi.org/10.1051/pmed/2021028.
Radigois, Jean-Yves. "Quand le travailleur social intervient dans un contexte à caractère sectaire1." 41, no. 2 (November 26, 2008): 31–51. http://dx.doi.org/10.7202/019432ar.
Nascimento, Fernando, James Broda, and Dylan Hayton-Ruffner. "Concept Detection in Philosophical Corpora." Digital Studies/le champ numérique (DSCN) Open Issue 2022 12, no. 1 (May 5, 2022). http://dx.doi.org/10.16995/dscn.8106.
Poirier, Sylvie. "Ontologie." Anthropen, 2016. http://dx.doi.org/10.17184/eac.anthropen.035.
Dissertations / Theses on the topic "Détection des Dérives Conceptuelles":
Albakour, Subhy. "Stream-automl : automated machine learning overimbalanced data streams for bipartite ranking problems." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT015.
Despite its popularity in the scientific literature, stream learning has yet to substantiate its practical utility in industrial applications. Characterized by the incessant influx of high-velocity, voluminous, and dynamically changing data, online marketing seems to be the favorite candidate for stream learning to make its entry into the industry. In this context, state-of-theart stream learning is of little utility, as it mainly focuses on classification, while bipartite ranking constitutes better modeling of the problem of online marketing. Recently, the combination of stream learning and AutoML, i.e., Stream-AutoML, has been drawing more attention from the scientific community. This work investigates the applicability of Stream-AutoML to bipartite ranking problems when data is imbalanced. We commence by developing a framework to execute and evaluate Stream-AutoML pipelines of stream learning models. Then we propose a framework for computing AUC-ROC incrementally, as well as introducing exponential decay to serve as a forgetting mechanism. We also propose a framework for concept drift detection using AUC-ROC, for which we develop six statistical tests for differences in AUC-ROC with theoretical bounds of type I and type II errors. Finally, we propose four data generators that enrich the tool kit to evaluate concept drift detectors under controlled environments. Results have shown that the proposed methods reduce the resources allocated for evaluation considerably and detect concept drifts with very small false positives. These contributions prepare the field for Stream-AutoML to solve bipartite ranking problems, which can be then exploited in online marketing applications. Optimized implementations of the proposed methods were developed and have already been adopted in the online marketing product of IDAaaS
Kanso, Hussein. "Nouveaux outils de détection d'estrogenes dans les eaux : synthèse de dérives électroactifs et développement d'immunocapteurs." Perpignan, 2014. http://www.theses.fr/2013PERP1246.
Neyns, Valérie. "Les modalités du contrôle cognitif en situation dynamique : anticipation et gestion des dérives : le cas de l'anesthésie." Phd thesis, Université Toulouse le Mirail - Toulouse II, 2011. http://tel.archives-ouvertes.fr/tel-00628433.
Costa, Annie. "Intérêt de l'analyse quantitative de la queuosine et de ses dérives par CLHP dans la détection et l'exploration in vitro et in vivo d'une hypomodification des ARNt porteurs de ces nucléosides hypermodifiés." Dijon, 2002. http://www.theses.fr/2002DIJOMU05.
Abichou, Bouthaina. "Contribution à la formalisation de bilans / états de santé multi-niveaux d'un système pour aider à la prise de décision en maintenance : agrégation d'indicateurs par l'intégrale de Choquet." Phd thesis, Université de Lorraine, 2013. http://tel.archives-ouvertes.fr/tel-00839731.
Abichou, Bouthaïna. "Contribution à la formalition de bilans/états de santé multi-niveaux d'un système pour aider à la prise de décision en maintenance : agrégation d'indicateurs par l'intégrale de Choquet." Thesis, Université de Lorraine, 2013. http://www.theses.fr/2013LORR0020/document.
This work is addressing the health assessment of a multi-component system by means of multi-levels health check-up. Thus scientific Ph. D. objective aims to establish items of a generic health check-up concept. It focuses specifically on the functions of anomaly detection, normalization and aggregation of different indicators to develop a synthetic index representing the overall health status for each element within the system. In that way, it is proposed a new approach for detecting conditional anomalies. This approach has the advantage of quantifying the deviation for each indicator compared to its nominal behavior while taking into account the context in which the system operates. An extension of the Choquet integral used as an operator aggregating indicators is also proposed. This extension regards on the one hand, a process of an unsupervised learning of the capacity coefficients for the lowest level of abstraction, namely components level, and on the other hand, an approach to inference them from one level to another. These contributions are implemented on a ship diesel engine which is the most critical system for the BMCI project of the MER-PACA pole to which this thesis is attached
Books on the topic "Détection des Dérives Conceptuelles":
L, Commons Michael, and Symposium on Quantitative Analyses of Behavior. (8th : 1985 : Harvard University), eds. Computational and clinical approaches to pattern recognition and concept formation. Hillsdale, N.J: Lawrence Erlbaum Associates, 1990.
L, Commons Michael, and Symposium on Quantitative Analyses of Behavior., eds. Behavioral approaches to pattern recognition and concept formation. Hillsdale, NJ: Erlbaum Associates, 1990.
(Editor), Michael L. Commons, John A. Nevin (Editor), Michael C. Davison (Editor), and Michael Davidson (Editor), eds. Signal Detection: Mechanisms, Models, and Applications (Quantitative Analyses of Behavior). Lawrence Erlbaum, 1991.
(Editor), Michael L. Commons, Richard J. Herrnstein (Editor), Stephen M. Kosslyn (Editor), and David B. Mumford (Editor), eds. Computational and Clinical Approaches to Pattern Recognition and Concept Formation: Quantitative Analyses of Behavior, Volume IX (Quantitative Analyses of Behavior). Lawrence Erlbaum, 1990.
(Editor), Michael L. Commons, Stephen Grossberg (Editor), and John E.R. Staddon (Editor), eds. Neural Network Models of Conditioning and Action: Quantitative Analyses of Behavior (Quantitative Analysis of Behavior Series). Lawrence Erlbaum, 1991.
(Editor), Michael L. Commons, James E. Mazur (Editor), John A. Nevin (Editor), and Howard Rachlin (Editor), eds. The Effect of Delay and of Intervening Events on Reinforcement Value: Quantitative Analyses of Behavior, Volume V (Quantitative Analyses of Behavior). Lawrence Erlbaum, 1986.
(Editor), Michael L. Commons, Stephen Grossberg (Editor), and John E.R. Staddon (Editor), eds. Neural Network Models of Conditioning and Action: Quantitative Analyses of Behavior. Lawrence Erlbaum, 1991.