Academic literature on the topic 'Apprentissage avec peu de données'
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Journal articles on the topic "Apprentissage avec peu de données"
Tomé, Mario. "Prononciation, littérature et apprentissage du français langue étrangère avec les medias sociaux." Anales de Filología Francesa 28, no. 1 (October 23, 2020): 673–88. http://dx.doi.org/10.6018/analesff.430211.
Full textBOCQUIER, F., N. DEBUS, A. LURETTE, C. MATON, G. VIUDES, C. H. MOULIN, and M. JOUVEN. "Elevage de précision en systèmes d’élevage peu intensifiés." INRAE Productions Animales 27, no. 2 (June 2, 2014): 101–12. http://dx.doi.org/10.20870/productions-animales.2014.27.2.3058.
Full textTOUATI, Y., T.-T. PHAN, S. BROSSIER, F. ADELINE-DUFLOT, M. NEAGOE, and E. FERRAT. "MISE EN OEUVRE D UNE SUPERVISION PAR OBSERVATION DIRECTE AVEC ENREGISTREMENT VIDEO EN SITUATION AUTHENTIQUE DE SOINS." EXERCER 32, no. 177 (November 1, 2021): 424–30. http://dx.doi.org/10.56746/exercer.2021.177.424.
Full textPerron, Denis, and Renee Cloutier. "L'insertion professionnelle des finissantes et des finissants du Conservatoire de musique du Québec." Canadian Journal of Higher Education 27, no. 1 (April 30, 1997): 69–104. http://dx.doi.org/10.47678/cjhe.v27i1.183296.
Full textسعاد عامر محمد. "Enseigner/Apprendre le français langue étrangère Etude de cas : Professeurs de français à Benghazi." Journal of Human Sciences 22, no. 4 (December 30, 2023): 140–46. http://dx.doi.org/10.51984/johs.v22i4.2964.
Full textLebrun, Johanne. "Des objectifs aux compétences : quelles incidences sur les démarches d’enseignement-apprentissage des manuels scolaires en sciences humaines* ?" Revue des sciences de l'éducation 35, no. 2 (December 16, 2009): 15–36. http://dx.doi.org/10.7202/038727ar.
Full textÖnen, Melek, Francesco Cremonesi, and Marco Lorenzi. "Apprentissage automatique fédéré pour l’IA collaborative dans le secteur de la santé." Revue internationale de droit économique XXXVI, no. 3 (April 21, 2023): 95–113. http://dx.doi.org/10.3917/ride.363.0095.
Full textFlores-Espejo, Julia. "Vivencia de Aprendizaje sobre Significados de Naturaleza de la Ciencia en un Postgrado: Mirada Fenomenológica." GACETA DE PEDAGOGÍA, no. 37 (December 1, 2018): 191–220. http://dx.doi.org/10.56219/rgp.vi37.731.
Full textGirard, Geneviève, Annie Plourde, Caroline Morin, Nathalie Martel, and Cynthia Gagnon. "Un environnement supportant à deux voies : développement d’une formation sur le traumatisme craniocérébral pour le milieu scolaire." Développement Humain, Handicap et Changement Social 20, no. 1 (February 28, 2022): 31–39. http://dx.doi.org/10.7202/1086766ar.
Full textTartas, Valérie, Anne Nelly Perret-Clermont, Pascale Marro, and Michèle Grossen. "Interactions sociales et appropriation de stratégies par l’enfant pour résoudre un problème : quelles méthodes ?" Bulletin de psychologie 57, no. 469 (2004): 111–15. http://dx.doi.org/10.3406/bupsy.2004.15311.
Full textDissertations / Theses on the topic "Apprentissage avec peu de données"
Gautheron, Léo. "Construction de Représentation de Données Adaptées dans le Cadre de Peu d'Exemples Étiquetés." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSES044.
Full textMachine learning consists in the study and design of algorithms that build models able to handle non trivial tasks as well as or better than humans and hopefully at a lesser cost.These models are typically trained from a dataset where each example describes an instance of the same task and is represented by a set of characteristics and an expected outcome or label which we usually want to predict.An element required for the success of any machine learning algorithm is related to the quality of the set of characteristics describing the data, also referred as data representation or features.In supervised learning, the more the features describing the examples are correlated with the label, the more effective the model will be.There exist three main families of features: the ``observable'', the ``handcrafted'' and the ``latent'' features that are usually automatically learned from the training data.The contributions of this thesis fall into the scope of this last category. More precisely, we are interested in the specific setting of learning a discriminative representation when the number of data of interest is limited.A lack of data of interest can be found in different scenarios.First, we tackle the problem of imbalanced learning with a class of interest composed of a few examples by learning a metric that induces a new representation space where the learned models do not favor the majority examples.Second, we propose to handle a scenario with few available examples by learning at the same time a relevant data representation and a model that generalizes well through boosting models using kernels as base learners approximated by random Fourier features.Finally, to address the domain adaptation scenario where the target set contains no label while the source examples are acquired in different conditions, we propose to reduce the discrepancy between the two domains by keeping only the most similar features optimizing the solution of an optimal transport problem between the two domains
Barrère, Killian. "Architectures de Transformer légères pour la reconnaissance de textes manuscrits anciens." Electronic Thesis or Diss., Rennes, INSA, 2023. http://www.theses.fr/2023ISAR0017.
Full textTransformer architectures deliver low error rates but are challenging to train due to limited annotated data in handwritten text recognition. We propose lightweight Transformer architectures to adapt to the limited amounts of annotated handwritten text available. We introduce a fast Transformer architecture with an encoder, processing up to 60 pages per second. We also present architectures using a Transformer decoder to incorporate language modeling into character recognition. To effectively train our architectures, we offer algorithms for generating synthetic data adapted to the visual style of modern and historical documents. Finally, we propose strategies for learning with limited data and reducing prediction errors. Our architectures, combined with synthetic data and these strategies, achieve competitive error rates on lines of text from modern documents. For historical documents, they train effectively with minimal annotated data, surpassing state-ofthe- art approaches. Remarkably, just 500 annotated lines are sufficient for character error rates close to 5%
Kasper, Kévin. "Apprentissage d'estimateurs sans modèle avec peu de mesures - Application à la mécanique des fluides." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLN029/document.
Full textThis thesis deals with sparsity promoting techniques in order to produce efficient estimators relying only on a small amount of measurements given by sensors. These sensor locations are crucial to the estimators and have to be chosen meticulously. The proposed methods do not require dynamical models and are instead based on a collection of snapshots of the field of interest. This learning sequence can be acquired through measurements on the real system or through numerical simulation. By relying only on a learning sequence, and not on dynamical models, the proposed methods become general and applicable to a variety of systems.These techniques are illustrated on the 2-D fluid flow around a cylindrical body. The pressure field in the neighbourhood of the cylinder has to be estimated from a limited amount of surface pressure measurements. For a given arrangement of the sensors, efficient estimators suited to these locations are proposed. These estimators fully harness the information given by the limited amount of sensors by manipulating sparse representations and classes. Cases where the measurements are no longer made on the field to be estimated can also be considered. A sensor placement algorithm is proposed in order to improve the performances of the estimators.Multiple extensions are discussed : incorporating past measurements, past control inputs, recovering a field non-linearly related to the measurements, estimating a vectorial field, etc
Dabuleanu, Simona. "Problèmes aux limites pour les équations de Hamilton-Jacobi avec viscosité et données initiales peu régulières." Nancy 1, 2003. http://www.theses.fr/2003NAN10058.
Full textThis thesis deal with the viscous Hamilton-Jacobi equations (VHJ) on bounded domains with smooth boundary. This equation is a nonlinear parabolic problem for which the second term is a power of the gradient of the solution. We study the existence, uniqueness and regularity of weak solutions for (VHJ) equation with Dirichlet or Neumann homogeneous boundary conditions and irregular initial data. The cases of initial data a bounded Radon measure, or a measurable function in the Lebesgue space are investigated. Next, using the Bernstein technique we prove some qualitative properties of these solutions. A particular attention is given to the long time behaviour depending on the sign and the exponent of the nonlinear term
Tremblay, Maxime. "Vision numérique avec peu d'étiquettes : segmentation d'objets et analyse de l'impact de la pluie." Doctoral thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/69039.
Full textPhan, Thi Hai Hong. "Reconnaissance d'actions humaines dans des vidéos avec l'apprentissage automatique." Thesis, Cergy-Pontoise, 2019. http://www.theses.fr/2019CERG1038.
Full textIn recent years, human action recognition (HAR) has attracted the research attention thanks to its various applications such as intelligent surveillance systems, video indexing, human activities analysis, human-computer interactions and so on. The typical issues that the researchers are envisaging can be listed as the complexity of human motions, the spatial and temporal variations, cluttering, occlusion and change of lighting condition. This thesis focuses on automatic recognizing of the ongoing human actions in a given video. We address this research problem by using both shallow learning and deep learning approaches.First, we began the research work with traditional shallow learning approaches based on hand-scrafted features by introducing a novel feature named Motion of Oriented Magnitudes Patterns (MOMP) descriptor. We then incorporated this discriminative descriptor into simple yet powerful representation techniques such as Bag of Visual Words, Vector of locally aggregated descriptors (VLAD) and Fisher Vector to better represent actions. Also, PCA (Principal Component Analysis) and feature selection (statistical dependency, mutual information) are applied to find out the best subset of features in order to improve the performance and decrease the computational expense. The proposed method obtained the state-of-the-art results on several common benchmarks.Recent deep learning approaches require an intensive computations and large memory usage. They are therefore difficult to be used and deployed on the systems with limited resources. In the second part of this thesis, we present a novel efficient algorithm to compress Convolutional Neural Network models in order to decrease both the computational cost and the run-time memory footprint. We measure the redundancy of parameters based on their relationship using the information theory based criteria, and we then prune the less important ones. The proposed method significantly reduces the model sizes of different networks such as AlexNet, ResNet up to 70% without performance loss on the large-scale image classification task.Traditional approach with the proposed descriptor achieved the great performance for human action recognition but only on small datasets. In order to improve the performance on the large-scale datasets, in the last part of this thesis, we therefore exploit deep learning techniques to classify actions. We introduce the concepts of MOMP Image as an input layer of CNNs as well as incorporate MOMP image into deep neural networks. We then apply our network compression algorithm to accelerate and improve the performance of system. The proposed method reduces the model size, decreases the over-fitting, and thus increases the overall performance of CNN on the large-scale action datasets.Throughout the thesis, we have showed that our algorithms obtain good performance in comparison to the state-of-the-art on challenging action datasets (Weizmann, KTH, UCF Sports, UCF-101 and HMDB51) with low resource required
Raja, Suleiman Raja Fazliza. "Méthodes de detection robustes avec apprentissage de dictionnaires. Applications à des données hyperspectrales." Thesis, Nice, 2014. http://www.theses.fr/2014NICE4121/document.
Full textThis Ph.D dissertation deals with a "one among many" detection problem, where one has to discriminate between pure noise under H0 and one among L known alternatives under H1. This work focuses on the study and implementation of robust reduced dimension detection tests using optimized dictionaries. These detection methods are associated with the Generalized Likelihood Ratio test. The proposed approaches are principally assessed on hyperspectral data. In the first part, several technical topics associated to the framework of this dissertation are presented. The second part highlights the theoretical and algorithmic aspects of the proposed methods. Two issues linked to the large number of alternatives arise in this framework. In this context, we propose dictionary learning techniques based on a robust criterion that seeks to minimize the maximum power loss (type minimax). In the case where the learned dictionary has K = 1 column, we show that the exact solution can be obtained. Then, we propose in the case K > 1 three minimax learning algorithms. Finally, the third part of this manuscript presents several applications. The principal application regards astrophysical hyperspectral data of the Multi Unit Spectroscopic Explorer instrument. Numerical results show that the proposed algorithms are robust and in the case K > 1 they allow to increase the minimax detection performances over the K = 1 case. Other possible applications such as worst-case recognition of faces and handwritten digits are presented
Truong, Nguyen Tuong Vinh. "Apprentissage de fonctions d'ordonnancement avec peu d'exemples étiquetés : une application au routage d'information, au résumé de textes et au filtrage collaboratif." Paris 6, 2009. http://www.theses.fr/2009PA066568.
Full textBelilovsky, Eugene. "Apprentissage de graphes structuré et parcimonieux dans des données de haute dimension avec applications à l’imagerie cérébrale." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLC027.
Full textThis dissertation presents novel structured sparse learning methods on graphs that address commonly found problems in the analysis of neuroimaging data as well as other high dimensional data with few samples. The first part of the thesis proposes convex relaxations of discrete and combinatorial penalties involving sparsity and bounded total variation on a graph as well as bounded `2 norm. These are developed with the aim of learning an interpretable predictive linear model and we demonstrate their effectiveness on neuroimaging data as well as a sparse image recovery problem.The subsequent parts of the thesis considers structure discovery of undirected graphical models from few observational data. In particular we focus on invoking sparsity and other structured assumptions in Gaussian Graphical Models (GGMs). To this end we make two contributions. We show an approach to identify differences in Gaussian Graphical Models (GGMs) known to have similar structure. We derive the distribution of parameter differences under a joint penalty when parameters are known to be sparse in the difference. We then show how this approach can be used to obtain confidence intervals on edge differences in GGMs. We then introduce a novel learning based approach to the problem structure discovery of undirected graphical models from observational data. We demonstrate how neural networks can be used to learn effective estimators for this problem. This is empirically shown to be flexible and efficient alternatives to existing techniques
Vo, Xuan Thanh. "Apprentissage avec la parcimonie et sur des données incertaines par la programmation DC et DCA." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0193/document.
Full textIn this thesis, we focus on developing optimization approaches for solving some classes of optimization problems in sparsity and robust optimization for data uncertainty. Our methods are based on DC (Difference of Convex functions) programming and DCA (DC Algorithms) which are well-known as powerful tools in optimization. This thesis is composed of two parts: the first part concerns with sparsity while the second part deals with uncertainty. In the first part, a unified DC approximation approach to optimization problem involving the zero-norm in objective is thoroughly studied on both theoretical and computational aspects. We consider a common DC approximation of zero-norm that includes all standard sparse inducing penalty functions, and develop general DCA schemes that cover all standard algorithms in the field. Next, the thesis turns to the nonnegative matrix factorization (NMF) problem. We investigate the structure of the considered problem and provide appropriate DCA based algorithms. To enhance the performance of NMF, the sparse NMF formulations are proposed. Continuing this topic, we study the dictionary learning problem where sparse representation plays a crucial role. In the second part, we exploit robust optimization technique to deal with data uncertainty for two important problems in machine learning: feature selection in linear Support Vector Machines and clustering. In this context, individual data point is uncertain but varies in a bounded uncertainty set. Different models (box/spherical/ellipsoidal) related to uncertain data are studied. DCA based algorithms are developed to solve the robust problems
Books on the topic "Apprentissage avec peu de données"
Ontario. Esquisse de cours 12e année: Mathématiques de la gestion des données mdm4u cours préuniversitaire. Vanier, Ont: CFORP, 2002.
Find full textOntario. Esquisse de cours 12e année: Sciences de l'activité physique pse4u cours préuniversitaire. Vanier, Ont: CFORP, 2002.
Find full textOntario. Esquisse de cours 12e année: Technologie de l'information en affaires btx4e cours préemploi. Vanier, Ont: CFORP, 2002.
Find full textOntario. Esquisse de cours 12e année: Études informatiques ics4m cours préuniversitaire. Vanier, Ont: CFORP, 2002.
Find full textOntario. Esquisse de cours 12e année: Mathématiques de la technologie au collège mct4c cours précollégial. Vanier, Ont: CFORP, 2002.
Find full textOntario. Esquisse de cours 12e année: Sciences snc4m cours préuniversitaire. Vanier, Ont: CFORP, 2002.
Find full textOntario. Esquisse de cours 12e année: English eae4e cours préemploi. Vanier, Ont: CFORP, 2002.
Find full textOntario. Esquisse de cours 12e année: Le Canada et le monde: une analyse géographique cgw4u cours préuniversitaire. Vanier, Ont: CFORP, 2002.
Find full textOntario. Esquisse de cours 12e année: Environnement et gestion des ressources cgr4e cours préemploi. Vanier, Ont: CFORP, 2002.
Find full textOntario. Esquisse de cours 12e année: Histoire de l'Occident et du monde chy4c cours précollégial. Vanier, Ont: CFORP, 2002.
Find full textBook chapters on the topic "Apprentissage avec peu de données"
BOURNEL-BOSSON, Chae-Yeon, and Isabelle CROS. "Former les futurs enseignants de langue au numérique par l’approche réflexive (collaborative)." In Numérique et didactique des langues et cultures, 131–54. Editions des archives contemporaines, 2022. http://dx.doi.org/10.17184/eac.5758.
Full textAZAOUI, Brahim. "Entrer dans le langage par le plurilinguisme et la multimodalité." In Formation linguistique des apprenants allophones et pédagogies innovantes, 51–64. Editions des archives contemporaines, 2021. http://dx.doi.org/10.17184/eac.4178.
Full text"Saisie des données." In Introduction à l’analyse des données de sondage avec SPSS : Guide d’auto-apprentissage, 11–34. Presses de l'Université du Québec, 2008. http://dx.doi.org/10.2307/j.ctv18pgkjn.4.
Full textOUVRARD ANDRIANTSOA, Louise. "Le glossaire de Moodle." In Dictionnaires et apprentissage des langues, 89–102. Editions des archives contemporaines, 2021. http://dx.doi.org/10.17184/eac.4505.
Full textPERKO, Gregor, and Patrice Pognan. "Dictionnaire langue maternelle - langue étrangère." In Dictionnaires et apprentissage des langues, 15–24. Editions des archives contemporaines, 2021. http://dx.doi.org/10.17184/eac.4499.
Full textATTO, Abdourrahmane M., Héla HADHRI, Flavien VERNIER, and Emmanuel TROUVÉ. "Apprentissage multiclasse multi-étiquette de changements d’état à partir de séries chronologiques d’images." In Détection de changements et analyse des séries temporelles d’images 2, 247–71. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9057.ch6.
Full textBERK, Cybèle. "Enseigner la grammaire turque." In Enseignement-apprentissage de la grammaire en langue vivante étrangère, 21–32. Editions des archives contemporaines, 2023. http://dx.doi.org/10.17184/eac.5810.
Full text"Front Matter." In Introduction à l’analyse des données de sondage avec SPSS : Guide d’auto-apprentissage, I—VI. Presses de l'Université du Québec, 2008. http://dx.doi.org/10.2307/j.ctv18pgkjn.1.
Full text"Back Matter." In Introduction à l’analyse des données de sondage avec SPSS : Guide d’auto-apprentissage, 112. Presses de l'Université du Québec, 2008. http://dx.doi.org/10.2307/j.ctv18pgkjn.10.
Full text"Table of Contents." In Introduction à l’analyse des données de sondage avec SPSS : Guide d’auto-apprentissage, VII—X. Presses de l'Université du Québec, 2008. http://dx.doi.org/10.2307/j.ctv18pgkjn.2.
Full textConference papers on the topic "Apprentissage avec peu de données"
Romanet, I., J. H. Catherine, P. Laurent, R. Lan, and E. Dubois. "Efficacité de l’ostéotomie interalvéolaire par piezocision : revue de la littérature." In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206603010.
Full textNoaillon, E., S. Azogui-Lévy, G. Lescaille, R. Toledo, V. Descroix, P. Goudot, and J. Rochefort. "Impact des recommandations de l’ANSM dans la prise en charge en cabinet libéral des collections circonscrites aiguës suppurées de la cavité orale d’origine dentaire : enquête nationale." In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206602017.
Full textReports on the topic "Apprentissage avec peu de données"
Brinkerhoff, Derick W., Sarah Frazer, and Lisa McGregor. S'adapter pour apprendre et apprendre pour s'adapter : conseils pratiques tirés de projets de développement internationaux. RTI Press, January 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0015.1801.fr.
Full textFonseca, Raquel, and Markus Poschke. L’évolution et la composition de la richesse des ménages Québécois. CIRANO, September 2023. http://dx.doi.org/10.54932/hqtc4594.
Full textRipoll, Santiago, Tabitha Hrynick, Ashley Ouvrier, Megan Schmidt-Sane, Federico Marco Federici, and Elizabeth Storer. 10 façons dont les gouvernements locaux en milieu urbain multiculturel peuvent appuyer l’égalité vaccinale en cas de pandémie. SSHAP, January 2023. http://dx.doi.org/10.19088/sshap.2023.001.
Full textRousseau, Henri-Paul. Gutenberg, L’université et le défi numérique. CIRANO, December 2022. http://dx.doi.org/10.54932/wodt6646.
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