Academic literature on the topic 'Recommandation de données d'apprentissage'
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Journal articles on the topic "Recommandation de données d'apprentissage":
Gaffield, Scott M. "Justice Not Done : The Hanging of Elizabeth Workman." Canadian journal of law and society 20, no. 1 (April 2005): 171–92. http://dx.doi.org/10.1353/jls.2006.0007.
Lubis, Nuriaty Prasetya, Junita Friska, and Zulherman Zulherman. "Développement De La Méthode De Français Basé Sur Google Classroom En Classe XI Au SMA Negeri 21 Medan." HEXAGONE Jurnal Pendidikan, Linguistik, Budaya dan Sastra Perancis 10, no. 2 (December 29, 2021): 34. http://dx.doi.org/10.24114/hxg.v10i2.30299.
Jerbi, Houssem, Geneviève Pujolle, Franck Ravat, and Olivier Teste. "Recommandation de requêtes dans les bases de données multidimensionnelles annotées." Ingénierie des systèmes d'information 16, no. 1 (February 2011): 113–38. http://dx.doi.org/10.3166/isi.16.1.113-138.
Quach, Caroline, and Shelley Deeks. "La vaccination contre la COVID-19 : pourquoi allonger l’intervalle entre les doses?" Official Journal of the Association of Medical Microbiology and Infectious Disease Canada 6, no. 2 (July 2021): 79–84. http://dx.doi.org/10.3138/jammi-2021-0323.fr.
Goupil, Georgette, Michelle Comeau, and Pierre Michaud. "Étude descriptive et exploratoire sur les services offerts aux élèves en difficulté d’apprentissage." Articles 20, no. 4 (October 10, 2007): 645–56. http://dx.doi.org/10.7202/031760ar.
Racine, Guylaine. "La construction de savoirs d'expérience chez des intervenantes d'organismes communautaires pour femmes sans-abri : un processus participatif, collectif et non planifié1." Nouvelles pratiques sociales 13, no. 1 (October 2, 2002): 69–84. http://dx.doi.org/10.7202/000006ar.
Giardina, Max, Denis Harvey, and Martine Mottet. "L’évaluation des SAMI (système d’apprentissage multimédia interactif) : de la théorie à la pratique." Articles 24, no. 2 (April 30, 2008): 335–53. http://dx.doi.org/10.7202/502015ar.
Chehata, Nesrine, Karim Ghariani, Arnaud Le Bris, and Philippe Lagacherie. "Apport des images pléiades pour la délimitation des parcelles agricoles à grande échelle." Revue Française de Photogrammétrie et de Télédétection, no. 209 (January 29, 2015): 165–71. http://dx.doi.org/10.52638/rfpt.2015.220.
Lavoie-Tremblay, Mélanie, and Malcolm Anderson. "Le programme de Formation en recherche pour cadres qui exercent dans la santé (FORCES): Perceptions de la première cohorte de boursiers." Healthcare Management Forum 20, no. 2 (July 2007): 14–19. http://dx.doi.org/10.1016/s0840-4704(10)60508-x.
Weisser, Marc. "Les problèmes d’arithmétique : traits de surface, modes de résolution et taux de réussite." Articles 25, no. 2 (October 17, 2007): 375–99. http://dx.doi.org/10.7202/032006ar.
Dissertations / Theses on the topic "Recommandation de données d'apprentissage":
Joshi, Bikash. "Algorithmes d'apprentissage pour les grandes masses de données : Application à la classification multi-classes et à l'optimisation distribuée asynchrone." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM046/document.
This thesis focuses on developing scalable algorithms for large scale machine learning. In this work, we present two perspectives to handle large data. First, we consider the problem of large-scale multiclass classification. We introduce the task of multiclass classification and the challenge of classifying with a large number of classes. To alleviate these challenges, we propose an algorithm which reduces the original multiclass problem to an equivalent binary one. Based on this reduction technique, we introduce a scalable method to tackle the multiclass classification problem for very large number of classes and perform detailed theoretical and empirical analyses.In the second part, we discuss the problem of distributed machine learning. In this domain, we introduce an asynchronous framework for performing distributed optimization. We present application of the proposed asynchronous framework on two popular domains: matrix factorization for large-scale recommender systems and large-scale binary classification. In the case of matrix factorization, we perform Stochastic Gradient Descent (SGD) in an asynchronous distributed manner. Whereas, in the case of large-scale binary classification we use a variant of SGD which uses variance reduction technique, SVRG as our optimization algorithm
Labiadh, Mouna. "Méthodologie de construction de modèles adaptatifs pour la simulation énergétique des bâtiments." Thesis, Lyon, 2021. http://www.theses.fr/2021LYSE1158.
Predictive modeling of energy consumption in buildings is essential for intelligent control and efficient planning of energy networks. One way to perform predictive modeling is through machine learning approaches. Alongside their good performance, these approaches are time efficient and facilitates the integration of buildings into smart environments. However, accurate machine learning models rely heavily on collecting relevant building operational data in a sufficient amount, notably when deep learning is used. In the field of buildings energy, historical data are not available for training, such is the case in newly built or newly renovated buildings. Moreover, it is common to verify the energy efficiency of buildings before construction or renovation. For such cases, only a contextual description about the future building and its design is available. The goal of this dissertation is to address the predictive modeling tasks of building energy consumption when no historical data are available for the given target building. To that end, existing data collected from multiple different source buildings are leveraged. This is increasingly relevant with the growth of open data initiatives in various sectors, namely building energy. The main idea is to transfer knowledge across building models. There is little research at the intersection of building energy modeling and knowledge transfer. An important challenge arises when dealing with multi-source data, since large domain shift may exist between different sources and also between each source and the target. As a contribution, a two-fold query-adaptive methodology is developed for cross-building predictive modeling. The first process recommends relevant training data to a target building solely by using a minimal contextual description on it (metadata). Contextual descriptions are provided as user queries. To enable a task-specific recommendation, a deep similarity learning framework is used. The second process trains multiple predictive models based on recommended training data. These models are combined together using an ensemble learning framework to ensure a robust performance. The implementation of the proposed methodology is based on microservices. Logically independent workflows are modeled as microservices with single purposes and separate data sources. Building metadata and time series data collected from multiple sources are integrated into an unified ontology-based view. Experimental evaluation of the predictive model factory validates the effectiveness and the applicability for the use case of building energy modeling. Moreover, because of its generic design, the methodology for query-adaptive cross-domain predictive modeling can be re-used for a diverse range of use cases in different fields
Servajean, Maximilien. "Recommandation diversifiée et distribuée pour les données scientifiques." Thesis, Montpellier 2, 2014. http://www.theses.fr/2014MON20216/document.
In many fields, novel technologies employed in information acquisition and measurement (e.g. phenotyping automated greenhouses) are at the basis of a phenomenal creation of data. In particular, we focus on two real use cases: plants observations in botany and phenotyping data in biology. Our contributions can be, however, generalized to Web data. In addition to their huge volume, data are also distributed. Indeed, each user stores their data in many heterogeneous sites (e.g. personal computers, servers, cloud); yet he wants to be able to share them. In both use cases, collaborative solutions, including distributed search and recommendation techniques, could benefit to the user.Thus, the global objective of this work is to define a set of techniques enabling sharing and discovery of data in heterogeneous distributed environment, through the use of search and recommendation approaches.For this purpose, search and recommendation allow users to be presented sets of results, or recommendations, that are both relevant to the queries submitted by the users and with respect to their profiles. Diversification techniques allow users to receive results with better novelty while avoiding redundant and repetitive content. By introducing a distance between each result presented to the user, diversity enables to return a broader set of relevant items.However, few works exploit profile diversity, which takes into account the users that share each item. In this work, we show that in some scenarios, considering profile diversity enables a consequent increase in results quality: surveys show that in more than 75% of the cases, users would prefer profile diversity to content diversity.Additionally, in order to address the problems related to data distribution among heterogeneous sites, two approaches are possible. First, P2P networks aim at establishing links between peers (nodes of the network): creating in this way an overlay network, where peers directly connected to a given peer p are known as his neighbors. This overlay is used to process queries submitted by each peer. However, in state of the art solutions, the redundancy of the peers in the various neighborhoods limits the capacity of the system to retrieve relevant items on the network, given the queries submitted by the users. In this work, we show that introducing diversity in the computation of the neighborhood, by increasing the coverage, enables a huge gain in terms of quality. By taking into account diversity, each peer in a given neighborhood has indeed, a higher probability to return different results given a keywords query compared to the other peers in the neighborhood. Whenever a query is submitted by a peer, our approach can retrieve up to three times more relevant items than state of the art solutions.The second category of approaches is called multi-site. Generally, in state of the art multi-sites solutions, the sites are homogeneous and consist in big data centers. In our context, we propose an approach enabling sharing among heterogeneous sites, such as small research teams servers, personal computers or big sites in the cloud. A prototype regrouping all contributions have been developed, with two versions addressing each of the use cases considered in this thesis
Collobert, Ronan. "Algorithmes d'Apprentissage pour grandes bases de données." Paris 6, 2004. http://www.theses.fr/2004PA066063.
Pradel, Bruno. "Evaluation des systèmes de recommandation à partir d'historiques de données." Paris 6, 2013. http://www.theses.fr/2013PA066263.
This thesis presents various experimental protocols leading to abetter offline estimation of errors in recommender systems. As a first contribution, results form a case study of a recommendersystem based on purchased data will be presented. Recommending itemsis a complex task that has been mainly studied considering solelyratings data. In this study, we put the stress on predicting thepurchase a customer will make rather than the rating he will assign toan item. While ratings data are not available for many industries andpurchases data widely used, very few studies considered purchasesdata. In that setting, we compare the performances of variouscollaborative filtering models from the litterature. We notably showthat some changes the training and testing phases, and theintroduction of contextual information lead to major changes of therelative perfomances of algorithms. The following contributions will focus on the study of ratings data. Asecond contribution will present our participation to the Challenge onContext-Aware Movie Recommendation. This challenge provides two majorchanges in the standard ratings prediction protocol: models areevaluated conisdering ratings metrics and tested on two specificsperiod of the year: Christmas and Oscars. We provides personnalizedrecommendation modeling the short-term evolution of the popularitiesof movies. Finally, we study the impact of the observation process of ratings onranking evaluation metrics. Users choose the items they want to rateand, as a result, ratings on items are not observed at random. First,some items receive a lot more ratings than others and secondly, highratings are more likely to be oberved than poor ones because usersmainly rate the items they likes. We propose a formal analysis ofthese effects on evaluation metrics and experiments on the Yahoo!Musicdataset, gathering standard and randomly collected ratings. We showthat considering missing ratings as negative during training phaseleads to good performances on the TopK task, but these performancescan be misleading favoring methods modeling the popularities of itemsmore than the real tastes of users
Ben, Ellefi Mohamed. "La recommandation des jeux de données basée sur le profilage pour le liage des données RDF." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT276/document.
With the emergence of the Web of Data, most notably Linked Open Data (LOD), an abundance of data has become available on the web. However, LOD datasets and their inherent subgraphs vary heavily with respect to their size, topic and domain coverage, the schemas and their data dynamicity (respectively schemas and metadata) over the time. To this extent, identifying suitable datasets, which meet specific criteria, has become an increasingly important, yet challenging task to supportissues such as entity retrieval or semantic search and data linking. Particularlywith respect to the interlinking issue, the current topology of the LOD cloud underlines the need for practical and efficient means to recommend suitable datasets: currently, only well-known reference graphs such as DBpedia (the most obvious target), YAGO or Freebase show a high amount of in-links, while there exists a long tail of potentially suitable yet under-recognized datasets. This problem is due to the semantic web tradition in dealing with "finding candidate datasets to link to", where data publishers are used to identify target datasets for interlinking.While an understanding of the nature of the content of specific datasets is a crucial prerequisite for the mentioned issues, we adopt in this dissertation the notion of "dataset profile" - a set of features that describe a dataset and allow the comparison of different datasets with regard to their represented characteristics. Our first research direction was to implement a collaborative filtering-like dataset recommendation approach, which exploits both existing dataset topic proles, as well as traditional dataset connectivity measures, in order to link LOD datasets into a global dataset-topic-graph. This approach relies on the LOD graph in order to learn the connectivity behaviour between LOD datasets. However, experiments have shown that the current topology of the LOD cloud group is far from being complete to be considered as a ground truth and consequently as learning data.Facing the limits the current topology of LOD (as learning data), our research has led to break away from the topic proles representation of "learn to rank" approach and to adopt a new approach for candidate datasets identication where the recommendation is based on the intensional profiles overlap between differentdatasets. By intensional profile, we understand the formal representation of a set of schema concept labels that best describe a dataset and can be potentially enriched by retrieving the corresponding textual descriptions. This representation provides richer contextual and semantic information and allows to compute efficiently and inexpensively similarities between proles. We identify schema overlap by the help of a semantico-frequential concept similarity measure and a ranking criterion based on the tf*idf cosine similarity. The experiments, conducted over all available linked datasets on the LOD cloud, show that our method achieves an average precision of up to 53% for a recall of 100%. Furthermore, our method returns the mappings between the schema concepts across datasets, a particularly useful input for the data linking step.In order to ensure a high quality representative datasets schema profiles, we introduce Datavore| a tool oriented towards metadata designers that provides rankedlists of vocabulary terms to reuse in data modeling process, together with additional metadata and cross-terms relations. The tool relies on the Linked Open Vocabulary (LOV) ecosystem for acquiring vocabularies and metadata and is made available for the community
Shu, Wu. "Contributions à la détection des anomalies et au développement des systèmes de recommandation." Thèse, Université de Sherbrooke, 2012. http://hdl.handle.net/11143/6563.
Elati, Mohamed. "Apprentissage de réseaux de régulation génétique à partir de données d'expression." Paris 13, 2007. http://www.theses.fr/2007PA132031.
Bonis, Thomas. "Algorithmes d'apprentissage statistique pour l'analyse géométrique et topologique de données." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS459/document.
In this thesis, we study data analysis algorithms using random walks on neighborhood graphs, or random geometric graphs. It is known random walks on such graphs approximate continuous objects called diffusion processes. In the first part of this thesis, we use this approximation result to propose a new soft clustering algorithm based on the mode seeking framework. For our algorithm, we want to define clusters using the properties of a diffusion process. Since we do not have access to this continuous process, our algorithm uses a random walk on a random geometric graph instead. After proving the consistency of our algorithm, we evaluate its efficiency on both real and synthetic data. We then deal tackle the issue of the convergence of invariant measures of random walks on random geometric graphs. As these random walks converge to a diffusion process, we can expect their invariant measures to converge to the invariant measure of this diffusion process. Using an approach based on Stein's method, we manage to obtain quantitfy this convergence. Moreover, the method we use is more general and can be used to obtain other results such as convergence rates for the Central Limit Theorem. In the last part of this thesis, we use the concept of persistent homology, a concept of algebraic topology, to improve the pooling step of the bag-of-words approach for 3D shapes
Aleksandrova, Marharyta. "Factorisation de matrices et analyse de contraste pour la recommandation." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0080/document.
In many application areas, data elements can be high-dimensional. This raises the problem of dimensionality reduction. The dimensionality reduction techniques can be classified based on their aim: dimensionality reduction for optimal data representation and dimensionality reduction for classification, as well as based on the adopted strategy: feature selection and feature extraction. The set of features resulting from feature extraction methods is usually uninterpretable. Thereby, the first scientific problematic of the thesis is how to extract interpretable latent features? The dimensionality reduction for classification aims to enhance the classification power of the selected subset of features. We see the development of the task of classification as the task of trigger factors identification that is identification of those factors that can influence the transfer of data elements from one class to another. The second scientific problematic of this thesis is how to automatically identify these trigger factors? We aim at solving both scientific problematics within the recommender systems application domain. We propose to interpret latent features for the matrix factorization-based recommender systems as real users. We design an algorithm for automatic identification of trigger factors based on the concepts of contrast analysis. Through experimental results, we show that the defined patterns indeed can be considered as trigger factors
Books on the topic "Recommandation de données d'apprentissage":
Prud'Homme, Roger. Données d'observation et gestion de l'apprentissage: Guide à l'intention des communautés d'apprentissage professionnelles. Québec (Québec): Presses de l'Université du Québec, 2014.
L' Ordinateur, le traitement de texte et les bases de données comme outils d'apprentissage. Sainte-Foy, Qué: Éditions du 24 juillet, 1987.
Book chapters on the topic "Recommandation de données d'apprentissage":
ROCHDI, Sara, and Nadia EL OUESDADI. "Les étudiants et les pratiques numériques informelles: échange et collaboration sur le réseau social Facebook." In Langue(s) en mondialisation, 127–36. Editions des archives contemporaines, 2022. http://dx.doi.org/10.17184/eac.5204.
Conference papers on the topic "Recommandation de données d'apprentissage":
Pajot, T., S. Ketoff, and L. Bénichou. "Chirurgie implantaire guidée : acquisition, planification, modélisation et production d'un guide chirurgical. Mise en place d'une chaine numérique pour la création interne et l'utilisation de guides chirurgicaux." In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206602006.