Contents
Academic literature on the topic 'Prédiction séquentielle'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Prédiction séquentielle.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Prédiction séquentielle"
Chachuat, B., N. Roche, and M. A. Latifi. "Réduction du modèle ASM 1 pour la commande optimale des petites stations d'épuration à boues activées." Revue des sciences de l'eau 16, no. 1 (April 12, 2005): 5–26. http://dx.doi.org/10.7202/705496ar.
Full textWillinger, Marc, and Alexandra Rauchs. "Expérimentations sur les choix séquentiels : application à " l'effet irréversibilité"." Revue économique 47, no. 1 (January 1, 1996): 51–71. http://dx.doi.org/10.3917/reco.p1996.47n1.0051.
Full textFicheur, G., C. Dumesnil, A. Caron, and R. Beuscart. "Codage automatisé à partir des courriers hospitaliers, des résultats de biologie et des prescriptions médicamenteuses : construction automatisée et évaluation de règles de prédiction séquentielles." Revue d'Épidémiologie et de Santé Publique 62 (March 2014): S75—S76. http://dx.doi.org/10.1016/j.respe.2014.01.016.
Full textLanfant-Weybel, Karine, Chantal Michot, Romain Daveau, Pierre-Yves Milliez, Isabelle Auquit-Auckbur, Patrice Fardellone, Michel Brazier, et al. "L’expression synoviale de CD20 représente un nouveau facteur prédictif potentiel de la progression des érosions osseuses lors d’une arthrite très récente traitée par DMARD en monothérapie séquentielle – étude pilote à partir de la cohorte VErA." Revue du Rhumatisme 79, no. 5 (October 2012): 436–43. http://dx.doi.org/10.1016/j.rhum.2012.02.002.
Full textIdakari, C. N., A. M. Efunshile, I. E. Akase, C. S. Osuagwu, P. Oshun, and O. O. Oduyebo. "Evaluation of procalcitonin as a biomarker of bacterial sepsis in adult population in a tertiary healthcare facility in Lagos, Nigeria." African Journal of Clinical and Experimental Microbiology 23, no. 2 (May 13, 2022): 131–40. http://dx.doi.org/10.4314/ajcem.v23i2.
Full textNyobe, Samuel, Fabien Campillo, Serge Moto, and Vivien Rossi. "The one step fixed-lag particle smoother as a strategy to improve the prediction step of particle filtering." Revue Africaine de Recherche en Informatique et Mathématiques Appliquées Volume 39 - 2023 (December 14, 2023). http://dx.doi.org/10.46298/arima.10784.
Full textAdmin - JAIM. "Résumés des conférences JRANF 2021." Journal Africain d'Imagerie Médicale (J Afr Imag Méd). Journal Officiel de la Société de Radiologie d’Afrique Noire Francophone (SRANF). 13, no. 3 (November 17, 2021). http://dx.doi.org/10.55715/jaim.v13i3.240.
Full textDissertations / Theses on the topic "Prédiction séquentielle"
Stoltz, Gilles. "Information incomplète et regret interne en prédiction de suites inidividuelles." Paris 11, 2005. https://tel.archives-ouvertes.fr/tel-00009759.
Full textThis thesis takes place within the theory of prediction of individual sequences. The latter avoids any modelling of the data and aims at providing some techniques of robust prediction and discuss their possibilities, limitations, and difficulties. It considers issues arising from the machine learning as well as from the game-theory communities, and these are dealt with thanks to statistical techniques, including martingale concentration inequalities and minimax lower bound techniques. The obtained results consist, among others, in external and internal regret minimizing strategies for label-efficient prediction or in games with partial monitoring. Such strategies are valuable for the on-line pricing problem or for on-line bandwidth allocation. We then focus on internal regret for general convex losses. We consider first the case of on-line portfolio selection, for which simulations on real data are provided, and generalize later the results to show how players can learn correlated equilibria in games with compact sets of strategies
Stoltz, Gilles. "Information incomplète et regret interne en prédiction de suites individuelles." Phd thesis, Université Paris Sud - Paris XI, 2005. http://tel.archives-ouvertes.fr/tel-00009759.
Full textPrémillieu, Nathanaël. "Améliorer la performance séquentielle à l'ère des processeurs massivement multicœurs." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00914898.
Full textPrémillieu, Nathanaël. "Améliorer la performance séquentielle à l’ère des processeurs massivement multicœurs." Thesis, Rennes 1, 2013. http://www.theses.fr/2013REN1S071/document.
Full textComputers are everywhere and the need for always more computation power has pushed the processor architects to find new ways to increase performance. The today's tendency is to replicate execution core on the same die to parallelize the execution. If it goes on, processors will become manycores featuring hundred to a thousand cores. However, Amdahl's law reminds us that increasing the sequential performance will always be vital to increase global performance. A perfect way to increase sequential performance is to improve how branches are executed because they limit instruction level parallelism. The branch prediction is the most studied solution, its interest greatly depending on its accuracy. In the last years, this accuracy has been continuously improved up to reach a hardly exceeding limit. An other solution is to suppress the branches by replacing them with a construct based on predicated instructions. However, the execution of predicated instructions on out-of-order processors comes up with several problems like the multiple definition problem. This study investigates these two aspects of the branch treatment. The first part is about branch prediction. A way to improve it without increasing the accuracy is to reduce the coast of a branch misprediction. This is possible by exploiting control flow reconvergence and control independence. The work done on the wrong path on instructions common to the two paths is saved to be reused on the correct path. The second part is about predicated instructions. We propose a solution to the multiple definition problem by selectively predicting the predicate values. A selective replay mechanism is used to reduce the cost of a predicate misprediction
Kalaitzidis, Kleovoulos. "Advanced speculation to increase the performance of superscalar processors." Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S007.
Full textEven in the multicore era, making single cores faster is paramount to achieve high- performance computing, given the existence of programs that are either inherently sequential or expose non-negligible sequential parts. Sequential performance has been essentially improving with the scaling of the processor structures that enable instruction-level parallelism (ILP). However, as modern microarchitectures continue to extract more ILP by employing larger instruction windows, true data dependencies remain a major performance bottleneck. Value Prediction (VP) and Load-Address Prediction (LAP) are two developing techniques that allow to overcome this obstacle and harvest more ILP by enabling the execution of instructions in a data-wise speculative manner. This thesis proposes mechanisms that are related with VP and LAP and lead to effectively higher performance improvements. First, VP is examined in an ISA-aware manner, that discloses the impact of certain ISA particularities on the anticipated speedup. Second, a novel binary-based VP model is introduced, namely VSEP, that allows to exploit certain value patterns that although they are encountered frequently, they cannot be captured by previous works. VSEP improves the obtained speedup by 19% and also, by virtue of its structure, it mitigates the cost of predicting values wider than 64 bits. By adapting this approach to perform LAP allows to predict the memory addresses of 48% of the committed loads. Eventually, a microarchitecture that leverages carefully this LAP mechanism can execute 32% of the committed loads early
Ziat, Ali Yazid. "Apprentissage de représentation pour la prédiction et la classification de séries temporelles." Electronic Thesis or Diss., Paris 6, 2017. http://www.theses.fr/2017PA066324.
Full textThis thesis deals with the development of time series analysis methods. Our contributions focus on two tasks: time series forecasting and classification. Our first contribution presents a method of prediction and completion of multivariate and relational time series. The aim is to be able to simultaneously predict the evolution of a group of time series connected to each other according to a graph, as well as to complete the missing values in these series (which may correspond for example to a failure of a sensor during a given time interval). We propose to use representation learning techniques to forecast the evolution of the series while completing the missing values and taking into account the relationships that may exist between them. Extensions of this model are proposed and described: first in the context of the prediction of heterogeneous time series and then in the case of the prediction of time series with an expressed uncertainty. A prediction model of spatio-temporal series is then proposed, in which the relations between the different series can be expressed more generally, and where these can be learned.Finally, we are interested in the classification of time series. A joint model of metric learning and time-series classification is proposed and an experimental comparison is conducted
Heinrich, Franz. "Modélisation, prédiction et optimisation de la consommation énergétique d'applications MPI à l'aide de SimGrid." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM018/document.
Full textThe High-Performance Computing (HPC) community is currently undergoingdisruptive technology changes in almost all fields, including a switch towardsmassive parallelism with several thousand compute cores on a single GPU oraccelerator and new, complex networks. Powering a massively parallel machinebecomesThe energy consumption of these machines will continue to grow in the future,making energy one of the principal cost factors of machine ownership. This explainswhy even the classic metric "flop/s", generally used to evaluate HPC applicationsand machines, is widely regarded as to be replaced by an energy-centric metric"flop/watt".One approach to predict energy consumption is through simulation, however, a pre-cise performance prediction is crucial to estimate the energy faithfully. In this thesis,we contribute to the performance and energy prediction of HPC architectures. Wepropose an energy model which we have implemented in the open source SimGridsimulator. We validate this model by carefully and systematically comparing itwith real experiments. We leverage this contribution to both evaluate existingand propose new DVFS governors that are part*icularly designed to suit the HPCcontext
Bou, Rjeily Carine. "Data mining and learning for markers extraction to improve the medical monitoring platforms." Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCA012.
Full textThe World Health Organization accords that about 31 % of deaths worldwide are caused by heart diseases every year. Data mining is a process of extracting interesting non-trivial, previously unknownand potentially useful information from huge amount of data. Medical data mining is the science of investigating medical data (i.e. vital signs) to explore significant information. Analyzing and interpreting the huge amount of complicated data into an appropriate therapeutic diagnosis with the right results is quite challenging task. Still, the fact that it is possible to combine these factors up to a certain point and extract a usually successful treatment, prevention and recovery plan is a sign of the good things to come. Thanks to that, it is now possible to improve patients’ quality of life, prevent condition worsening while maintaining medical costs at the decrease. This explains the increasing popularity in the usage and application of machine learning techniques to analyze, predict and classify medical data. As a first contribution, we studied many sequential patterns algorithms that are promising techniques in exploring data and we classified them in order to choose an appropriate one for predicting Heart Failure classes and presence. After comparing all the algorithms and implementing them on the same medical dataset, the CPT+ a sequence prediction algorithm has been chosen as it gave the most accurate results reaching an accuracy of 90.5% in predicting heart failure and its classes. By using the CPT+ algorithm with real patients dataset, we predicted heart failure 10 to 12 days prior. Thereafter, we switched our studies to time series strategy, and worked on real data extracted from real patients. 5 parameters were extracted from 3 patients over the course of a few years. The Random Tree algorithm yielded more the 85% correct predictions of heart failure 7 days prior
Zuo, Jingwei. "Apprentissage de représentations et prédiction pour des séries-temporelles inter-dépendantes." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG038.
Full textTime series is a common data type that has been applied to enormous real-life applications, such as financial analysis, medical diagnosis, environmental monitoring, astronomical discovery, etc. Due to its complex structure, time series raises several challenges in their data processing and mining. The representation of time series plays a key role in data mining tasks and machine learning algorithms for time series. Yet, a few methods consider the interrelation that may exist between different time series when building the representation. Moreover, the time series mining requires considering not only the time series' characteristics in terms of data complexity but also the concrete application scenarios where the data mining task is performed to build task-specific representations.In this thesis, we will study different time series representation approaches that can be used in various time series mining tasks, while capturing the relationships among them. We focus specifically on modeling the interrelations between different time series when building the representations, which can be the temporal relationship within each data source or the inter-variable relationship between various data sources. Accordingly, we study the time series collected from various application contexts under different forms. First, considering the temporal relationship between the observations, we learn the time series in a dynamic streaming context, i.e., time series stream, for which the time series data is continuously generated from the data source. Second, for the inter-variable relationship, we study the multivariate time series (MTS) with data collected from multiple data sources. Finally, we study the MTS in the Smart City context, when each data source is given a spatial position. The MTS then becomes a geo-located time series (GTS), for which the inter-variable relationship requires more modeling efforts with the external spatial information. Therefore, for each type of time series data collected from distinct contexts, the interrelations between the time series observations are emphasized differently, on the temporal or (and) variable axis.Apart from the data complexity from the interrelations, we study various machine learning tasks on time series in order to validate the learned representations. The high-level learning tasks studied in this thesis consist of time series classification, semi-supervised time series learning, and time series forecasting. We show how the learned representations connect with different time series learning tasks under distinct application contexts. More importantly, we conduct the interdisciplinary study on time series by leveraging real-life challenges in machine learning tasks, which allows for improving the learning model's performance and applying more complex time series scenarios.Concretely, for these time series learning tasks, our main research contributions are the following: (i) we propose a dynamic time series representation learning model in the streaming context, which considers both the characteristics of time series and the challenges in data streams. We claim and demonstrate that the Shapelet, a shape-based time series feature, is the best representation in such a dynamic context; (ii) we propose a semi-supervised model for representation learning in multivariate time series (MTS). The inter-variable relationship over multiple data sources is modeled in a real-life context, where the data annotations are limited; (iii) we design a geo-located time series (GTS) representation learning model for Smart City applications. We study specifically the traffic forecasting task, with a focus on the missing-value treatment within the forecasting algorithm
Ziat, Ali Yazid. "Apprentissage de représentation pour la prédiction et la classification de séries temporelles." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066324/document.
Full textThis thesis deals with the development of time series analysis methods. Our contributions focus on two tasks: time series forecasting and classification. Our first contribution presents a method of prediction and completion of multivariate and relational time series. The aim is to be able to simultaneously predict the evolution of a group of time series connected to each other according to a graph, as well as to complete the missing values in these series (which may correspond for example to a failure of a sensor during a given time interval). We propose to use representation learning techniques to forecast the evolution of the series while completing the missing values and taking into account the relationships that may exist between them. Extensions of this model are proposed and described: first in the context of the prediction of heterogeneous time series and then in the case of the prediction of time series with an expressed uncertainty. A prediction model of spatio-temporal series is then proposed, in which the relations between the different series can be expressed more generally, and where these can be learned.Finally, we are interested in the classification of time series. A joint model of metric learning and time-series classification is proposed and an experimental comparison is conducted