Literatura académica sobre el tema "Données étiquetées"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Données étiquetées".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Données étiquetées"
Brunet, Etienne. "Qui lemmatise dilemme attise". Scolia 13, n.º 1 (2000): 7–32. http://dx.doi.org/10.3406/scoli.2000.1218.
Texto completoEmbarki, Mohamed, Oussama Barakat, Thibaut Desmettre y Stephan Robert-Nicoud. "Extraction de la prosodie émotionnelle des appels téléphoniques aux services de régulation médicale des urgences des hôpitaux en France et en Suisse". Langages N° 234, n.º 2 (29 de mayo de 2024): 135–56. http://dx.doi.org/10.3917/lang.234.0135.
Texto completoHoët-van Cauwenberghe, Christine y Alain Jacques. "Artisanat et commerce : l’apport des étiquettes de plomb inscrites découvertes à Arras (Nemetacum)". Revue des Études Anciennes 112, n.º 2 (2010): 295–317. http://dx.doi.org/10.3406/rea.2010.6669.
Texto completoShaffer, Ryan y Benjamin Shearn. "Performing Unsupervised Machine Learning on Intelligence: An Analysis of Colonial Kenya Reports". Études françaises de renseignement et de cyber N° 2, n.º 1 (4 de junio de 2024): 211–38. http://dx.doi.org/10.3917/efrc.232.0211.
Texto completoBlanco Escoda, Xavier. "Introduction". Langues & Parole 5 (30 de noviembre de 2020): 7–21. http://dx.doi.org/10.5565/rev/languesparole.61.
Texto completoMerolla, Jennifer L., Laura B. Stephenson y Elizabeth J. Zechmeister. "Can Canadians Take a Hint? The (In)Effectiveness of Party Labels as Information Shortcuts in Canada". Canadian Journal of Political Science 41, n.º 3 (septiembre de 2008): 673–96. http://dx.doi.org/10.1017/s0008423908080797.
Texto completoPelletier, Jean-François, Denise Fortin y Julie Bordeleau. "Pour nous, être citoyens à part entière, ça veut dire…". Santé mentale au Québec 39, n.º 1 (10 de julio de 2014): 311–24. http://dx.doi.org/10.7202/1025919ar.
Texto completoKehinde, O. O., G. E. O. Makinde, O. Agbato, O. O. Adebowale, O. J. Awoyomi y O. G. Fasanmi. "Evaluation of dynamics and prevalence of microbial flora of soaked dry meats (Kundi and Ponmo) in Nigeria". Nigerian Journal of Animal Production 48, n.º 6 (18 de enero de 2022): 77–87. http://dx.doi.org/10.51791/njap.v48i6.3278.
Texto completoPoulin, Carole y Maurice Lévesque. "Les représentations sociales des étiquettes associées à la maladie mentale". Santé mentale au Québec 20, n.º 1 (11 de septiembre de 2007): 119–36. http://dx.doi.org/10.7202/032336ar.
Texto completoBélenguier, Luc. "Hémiptères Pentatomoidea des collections du muséum Henri-Lecoq de Clermont-Ferrand". BIOM - Revue scientifique pour la biodiversité du Massif central 2, n.º 1 (1 de junio de 2021): 19–33. http://dx.doi.org/10.52497/biom.v2i1.285.
Texto completoTesis sobre el tema "Données étiquetées"
Leclerc, Gabriel. "Apprendre de données positives et non étiquetées : application à la segmentation et la détection d'évènements calciques". Master's thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/69813.
Texto completoTwo types of neurotransmission occur in brain’s neurons: evoked transmission and spontaneous transmission. Unlike the former, the role of spontaneous transmission on synaptic plasticity –a mechanism used to endow the brain learning and memory abilities – remain unclear. Spontaneous neurotransmissions are localized and randomly happening in neuron’s synapses. When such spontaneous events happen, so-called miniature synaptic Ca²⁺ transients(mSCT), second messenger calcium ions entered the spine, activating downstream signaling pathways of synaptic plasticity. Using calcium imaging of in vitro neuron enable spatiotemporal visual-ization of the entry of calcium ions. Resulting calcium videos enable quantitative study of mSCT’s impact on synaptic plasticity. However, mSCT localization in calcium imaging can be challenging due to their small size, their low intensity compared with the imaging noise and their inherent randomness. In this master’s thesis, we present a method for quantitative high-through put analysis of calcium imaging videos that limits the variability induced by human interventions to obtain evidence for characterizing the impact of mSCTs on synaptic plasticity. Based on a semi-automatic intensity thresholded detection (ITD) tool, we are able to generate data to train a fully convolutional neural network (FCN) to rapidly and automaticaly detect mSCT from calcium videos. Using ITD noisy segmentations as training data combine with a positive and unlabeled (PU) training schema, we leveraged FCN performances and could even detect previously undetected low instensity mSCTs missed by ITD. The FCN also provide better segmentation than ITD. We then characterized the impact of PU parameters such as the number of P and the ratio P:U. The trained FCN is bundled in a all-in-one pipeline to permit a high-thoughtput analysis of mSCT. The pipeline offers detection, segmentation,characterization and visualization of mSCTs as well as a software solution to manage multiple videos with different metadatas.
Magnan, Christophe Nicolas. "Apprentissage à partir de données diversement étiquetées pour l'étude du rôle de l'environnement local dans les interactions entre acides aminés". Aix-Marseille 1, 2007. http://www.theses.fr/2007AIX11022.
Texto completoThe 3D structure of proteins is constrained by some interactions between distant amino acids in the primary sequences. An accurate prediction of these bonds may be a step forward for the prediction of the 3D structure from sequences. A review of the literature raises questions about the role of the neighbourhood of bonded amino acids in the formation of these bonds. We show that we have to investigate uncommon learning frameworks to answer these questions. The first one is a particular case of semi-supervised learning, in which the only labelled data to learn from belong to one class, and the second one considers that the data are subject to class-conditional classification noise. We show that learning in these frameworks leads to ill-posed problems. We give some assumptions that make these problems well-posed. We propose adaptations of well-known methods to these learning frameworks. We apply them to try to answer the questions on the biological problem considered in this study
Pelletier, Charlotte. "Cartographie de l'occupation des sols à partir de séries temporelles d'images satellitaires à hautes résolutions : identification et traitement des données mal étiquetées". Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30241/document.
Texto completoLand surface monitoring is a key challenge for diverse applications such as environment, forestry, hydrology and geology. Such monitoring is particularly helpful for the management of territories and the prediction of climate trends. For this purpose, mapping approaches that employ satellite-based Earth Observations at different spatial and temporal scales are used to obtain the land surface characteristics. More precisely, supervised classification algorithms that exploit satellite data present many advantages compared to other mapping methods. In addition, the recent launches of new satellite constellations - Landsat-8 and Sentinel-2 - enable the acquisition of satellite image time series at high spatial and spectral resolutions, that are of great interest to describe vegetation land cover. These satellite data open new perspectives, but also interrogate the choice of classification algorithms and the choice of input data. In addition, learning classification algorithms over large areas require a substantial number of instances per land cover class describing landscape variability. Accordingly, training data can be extracted from existing maps or specific existing databases, such as crop parcel farmer's declaration or government databases. When using these databases, the main drawbacks are the lack of accuracy and update problems due to a long production time. Unfortunately, the use of these imperfect training data lead to the presence of mislabeled training instance that may impact the classification performance, and so the quality of the produced land cover map. Taking into account the above challenges, this Ph.D. work aims at improving the classification of new satellite image time series at high resolutions. The work has been divided into two main parts. The first Ph.D. goal consists in studying different classification systems by evaluating two classification algorithms with several input datasets. In addition, the stability and the robustness of the classification methods are discussed. The second goal deals with the errors contained in the training data. Firstly, methods for the detection of mislabeled data are proposed and analyzed. Secondly, a filtering method is proposed to take into account the mislabeled data in the classification framework. The objective is to reduce the influence of mislabeled data on the classification performance, and thus to improve the produced land cover map
Feng, Wei. "Investigation of training data issues in ensemble classification based on margin concept : application to land cover mapping". Thesis, Bordeaux 3, 2017. http://www.theses.fr/2017BOR30016/document.
Texto completoClassification has been widely studied in machine learning. Ensemble methods, which build a classification model by integrating multiple component learners, achieve higher performances than a single classifier. The classification accuracy of an ensemble is directly influenced by the quality of the training data used. However, real-world data often suffers from class noise and class imbalance problems. Ensemble margin is a key concept in ensemble learning. It has been applied to both the theoretical analysis and the design of machine learning algorithms. Several studies have shown that the generalization performance of an ensemble classifier is related to the distribution of its margins on the training examples. This work focuses on exploiting the margin concept to improve the quality of the training set and therefore to increase the classification accuracy of noise sensitive classifiers, and to design effective ensemble classifiers that can handle imbalanced datasets. A novel ensemble margin definition is proposed. It is an unsupervised version of a popular ensemble margin. Indeed, it does not involve the class labels. Mislabeled training data is a challenge to face in order to build a robust classifier whether it is an ensemble or not. To handle the mislabeling problem, we propose an ensemble margin-based class noise identification and elimination method based on an existing margin-based class noise ordering. This method can achieve a high mislabeled instance detection rate while keeping the false detection rate as low as possible. It relies on the margin values of misclassified data, considering four different ensemble margins, including the novel proposed margin. This method is extended to tackle the class noise correction which is a more challenging issue. The instances with low margins are more important than safe samples, which have high margins, for building a reliable classifier. A novel bagging algorithm based on a data importance evaluation function relying again on the ensemble margin is proposed to deal with the class imbalance problem. In our algorithm, the emphasis is placed on the lowest margin samples. This method is evaluated using again four different ensemble margins in addressing the imbalance problem especially on multi-class imbalanced data. In remote sensing, where training data are typically ground-based, mislabeled training data is inevitable. Imbalanced training data is another problem frequently encountered in remote sensing. Both proposed ensemble methods involving the best margin definition for handling these two major training data issues are applied to the mapping of land covers
Suwareh, Ousmane. "Modélisation de la pepsinolyse in vitro en conditions gastriques et inférence de réseaux de filiation de peptides à partir de données de peptidomique". Electronic Thesis or Diss., Rennes, Agrocampus Ouest, 2022. https://tel.archives-ouvertes.fr/tel-04059711.
Texto completoAddressing the current demographic challenges, “civilization diseases” and the possible depletion of food resources, require optimization of food utilization and adapting their conception to the specific needs of each target population. This requires a better understanding of the different stages of the digestion process. In particular, how proteins are hydrolyzed is a major issue, due to their crucial role in human nutrition. However, the probabilistic laws governing the action of pepsin, the first protease to act in the gastrointestinal tract, are still unclear.In a first approach based on peptidomic data, we demonstrate that the hydrolysis by pepsin of a peptidebond depends on the nature of the amino acid residues in its large neighborhood, but also on physicochemical and structural variables describing its environment. In a second step, and considering the physicochemical environment at the peptide level, we propose a nonparametric model of the hydrolysis by pepsin of these peptides, and an Expectation-Maximization type estimation algorithm, offering novel perspectives for the valorization of peptidomic data. In this dynamic approach, we integrate the peptide kinship network into the estimation procedure, which leads to a more parsimonious model that is also more relevant regarding biological interpretations
Gautheron, Léo. "Construction de Représentation de Données Adaptées dans le Cadre de Peu d'Exemples Étiquetés". Thesis, Lyon, 2020. http://www.theses.fr/2020LYSES044.
Texto completoMachine 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
Ettaleb, Mohamed. "Approche de recommandation à base de fouille de données et de graphes étiquetés multi-couches : contributions à la RI sociale". Electronic Thesis or Diss., Aix-Marseille, 2020. http://www.theses.fr/2020AIXM0588.
Texto completoIn general, the purpose of a recommendation system is to assist users in selecting relevant elements from a wide range of elements. In the context of the explosion in the number of academic publications available (books, articles, etc.) online, providing a personalized recommendation service is becoming a necessity. In addition, automatic book recommendation based on a query is an emerging theme with many scientific locks. It combines several issues related to information retrieval and data mining for the assessment of the degree of opportunity to recommend a book. This assessment must be made taking into account the query but also the user profile (reading history, interest, notes and comments associated with previous readings) and the entire collection to which the document belongs. Two main avenues have been addressed in this paper to deal with the problem of automatic book recommendation : - Identification of the user’s intentions from a query. - Recommendation of relevant books according to the user’s needs
Gerald, Thomas. "Representation Learning for Large Scale Classification". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS316.
Texto completoThe past decades have seen the rise of new technologies that simplify information sharing. Today, a huge part of the data is accessible to most users. In this thesis, we propose to study the problems of document annotation to ease access to information thanks to retrieved annotations. We will be interested in extreme classification-related tasks which characterizes the tasks of automatic annotation when the number of labels is important. Many difficulties arise from the size and complexity of this data: prediction time, storage and the relevance of the annotations are the most representative. Recent research dealing with this issue is based on three classification schemes: "one against all" approaches learning as many classifiers as labels; "hierarchical" methods organizing a simple classifier structure; representation approaches embedding documents into small spaces. In this thesis, we study the representation classification scheme. Through our contributions, we study different approaches either to speed up prediction or to better structure representations. In a first part, we will study discrete representations such as "ECOC" methods to speed up the annotation process. In a second step, we will consider hyperbolic embeddings to take advantage of the qualities of this space for the representation of structured data
Dalloux, Clément. "Fouille de texte et extraction d'informations dans les données cliniques". Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S050.
Texto completoWith the introduction of clinical data warehouses, more and more health data are available for research purposes. While a significant part of these data exist in structured form, much of the information contained in electronic health records is available in free text form that can be used for many tasks. In this manuscript, two tasks are explored: the multi-label classification of clinical texts and the detection of negation and uncertainty. The first is studied in cooperation with the Rennes University Hospital, owner of the clinical texts that we use, while, for the second, we use publicly available biomedical texts that we annotate and release free of charge. In order to solve these tasks, we propose several approaches based mainly on deep learning algorithms, used in supervised and unsupervised learning situations
Raoui, Younès. "Indexation d'une base de données images : Application à la localisation et la cartographie fondées sur des radio-étiquettes et des amers visuels pour la navigation d'un robot en milieu intérieur". Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2011. http://tel.archives-ouvertes.fr/tel-00633980.
Texto completoCapítulos de libros sobre el tema "Données étiquetées"
PERKO, Gregor y Patrice Pognan. "Dictionnaire langue maternelle - langue étrangère". En Dictionnaires et apprentissage des langues, 15–24. Editions des archives contemporaines, 2021. http://dx.doi.org/10.17184/eac.4499.
Texto completoATTO, Abdourrahmane M., Héla HADHRI, Flavien VERNIER y Emmanuel TROUVÉ. "Apprentissage multiclasse multi-étiquette de changements d’état à partir de séries chronologiques d’images". En 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.
Texto completoActas de conferencias sobre el tema "Données étiquetées"
Ordioni, U., G. Labrosse, F. Campana, R. Lan, J. H. Catherine y A. F. Albertini. "Granulomatose oro-faciale révélatrice d’une maladie de Crohn : présentation d’un cas". En 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206603017.
Texto completo