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Статті в журналах з теми "Bruit des ensembles de données":
Chouaf, Seloua, and Youcef Smara. "Méthode de sélection des bandes à base de l'Analyse en Composantes Indépendantes appliquée aux images hyperspectrales de télédétection." Revue Française de Photogrammétrie et de Télédétection, no. 204 (April 8, 2014): 57–62. http://dx.doi.org/10.52638/rfpt.2013.22.
Brahmi, Ghanem, and Mohammed Bougara. "Atténuation du ground roll par le filtre surface wave atténuation : application pour le cas des données sismiques." Boletín de Ciencias de la Tierra, no. 52 (January 22, 2023): 29–36. http://dx.doi.org/10.15446/rbct.105227.
Courgeau, Daniel. "Analyse des données biographiques erronées." Population Vol. 46, no. 1 (January 1, 1991): 89–104. http://dx.doi.org/10.3917/popu.p1991.46n1.0104.
Messing, Karen, Katherine Lippel, Susan Stock, and France Tissot. "Si le bruit rend sourd, rend-il nécessairement sourde ?" Revue multidisciplinaire sur l'emploi, le syndicalisme et le travail 6, no. 2 (October 13, 2011): 3–25. http://dx.doi.org/10.7202/1006119ar.
Py, Bernard. "Les données orales exolingues entre magma verbal, interlangue et langue." Cahiers du Centre de Linguistique et des Sciences du Langage, no. 23 (April 9, 2022): 51–54. http://dx.doi.org/10.26034/la.cdclsl.2007.1429.
Chagnaud, Clément, Philippe Garat, Paule-Annick Davoine, and Guylaine Brun-Trigaud. "Classification d’aires de dispersion à l’aide d’un facteur géographique." Revue Internationale de Géomatique 30, no. 1-2 (January 2020): 67–83. http://dx.doi.org/10.3166/rig.2020.00107.
PLOTARD, Christophe, Sylvain MOULHERAT, Catherine De ROINCÉ, and Jérémie CORNUAU. "Prendre en compte la pollution sonore dans une modélisation de dynamiques de populations d’espèces." Sciences Eaux & Territoires, no. 43 (October 8, 2023): 43–47. http://dx.doi.org/10.20870/revue-set.2023.43.7512.
Fangseu Badjio, Edwige, and François Poulet. "Théorie du consensus appliquée au prétraitement des ensembles de données." Revue d'intelligence artificielle 22, no. 3-4 (August 1, 2008): 383–400. http://dx.doi.org/10.3166/ria.22.383-400.
Gendron, Daniel. "La présence « viking » au Nunavik: beaucoup de bruit pour rien!" Études/Inuit/Studies 39, no. 2 (December 2, 2016): 295–303. http://dx.doi.org/10.7202/1038152ar.
Vergniault, Christophe, and Sylvain Pouliquen. "Retour d’expérience sur l’évolution des méthodes géophysiques pour construire un modèle de sol en mer, utilisable pour des projets géotechniques." E3S Web of Conferences 342 (2022): 01002. http://dx.doi.org/10.1051/e3sconf/202234201002.
Дисертації з теми "Bruit des ensembles de données":
Al, Jurdi Wissam. "Towards next generation recommender systems through generic data quality." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCD005.
Recommender systems are essential for filtering online information and delivering personalized content, thereby reducing the effort users need to find relevant information. They can be content-based, collaborative, or hybrid, each with a unique recommendation approach. These systems are crucial in various fields, including e-commerce, where they help customers find pertinent products, enhancing user experience and increasing sales. A significant aspect of these systems is the concept of unexpectedness, which involves discovering new and surprising items. This feature, while improving user engagement and experience, is complex and subjective, requiring a deep understanding of serendipitous recommendations for its measurement and optimization. Natural noise, an unpredictable data variation, can influence serendipity in recommender systems. It can introduce diversity and unexpectedness in recommendations, leading to pleasant surprises. However, it can also reduce recommendation relevance, causing user frustration. Therefore, it is crucial to design systems that balance natural noise and serendipity. Inconsistent user information due to natural noise can negatively impact recommender systems, leading to lower-quality recommendations. Current evaluation methods often overlook critical user-oriented factors, making noise detection a challenge. To provide powerful recommendations, it’s important to consider diverse user profiles, eliminate noise in datasets, and effectively present users with relevant content from vast data catalogs. This thesis emphasizes the role of serendipity in enhancing recommender systems and preventing filter bubbles. It proposes serendipity-aware techniques to manage noise, identifies algorithm flaws, suggests a user-centric evaluation method, and proposes a community-based architecture for improved performance. It highlights the need for a system that balances serendipity and considers natural noise and other performance factors. The objectives, experiments, and tests aim to refine recommender systems and offer a versatile assessment approach
Durand, Marianne. "Combinatoire analytique et algorithmique des ensembles de données." Phd thesis, Ecole Polytechnique X, 2004. http://pastel.archives-ouvertes.fr/pastel-00000810.
Pont, Mathieu. "Analysis of Ensembles of Topological Descriptors." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS436.
Topological Data Analysis (TDA) forms a collection of tools to generically, robustly and efficiently reveal implicit structural patterns hidden in complex datasets. These tools allow to compute a topological representation for each member of an ensemble of datasets by encoding its main features of interest in a concise and informative manner. A major challenge consists then in designing analysis tools for such ensembles of topological descriptors. Several tools have been well studied for persistence diagrams, one of the most used descriptor. However, they suffer from a lack of specificity, which can yield identical data representations for significantly distinct datasets. In this thesis, we aimed at developing more advanced analysis tools for ensembles of topological descriptors, capable of tackling the lack of discriminability of persistence diagrams and going beyond what was already available for these objects. First, we adapt to merge trees, descriptors having a better specificity, the tools already available for persistence diagrams such as distances, geodesics and barycenters. Then, we want to go beyond this notion of average being the barycenter in order to study the variability within an ensemble of topological descriptors. We then adapt the Principal Component Analysis framework to persistence diagrams and merge trees, resulting in a dimensionality reduction method that indicates which structures in the ensemble are most responsible for the variability. However, this framework allows only to detect linear patterns of variability in the ensemble. To tackle this we propose to generalize this framework to Auto-Encoder in order to detect non-linear, i.e. more complex, patterns in an ensemble of merge trees or persistence diagrams. Specifically, we propose a new neural network layer capable of processing natively these objects. We present applications of all this work in feature tracking in a time-varying ensemble, data reduction to compress an ensemble of topological descriptors, clustering to form homogeneous groups in an ensemble, and dimensionality reduction to create a visual map indicating how the data are organized regarding each other in the ensemble
Boudjeloud-Assala, Baya Lydia. "Visualisation et algorithmes génétiques pour la fouille de grands ensembles de données." Nantes, 2005. http://www.theses.fr/2005NANT2065.
We present cooperative approaches using interactive visualization methods and automatic dimension selection methods for knowledge discovery in databases. Most existing data mining methods work in an automatic way, the user is not implied in the process. We try to involve more significantly the user role in the data mining process in order to improve his confidence and comprehensibility of the obtained models or results. Furthermore, the size of data sets is constantly increasing, these methods must be able to deal with large data sets. We try to improve the performances of the algorithms to deal with these high dimensional data sets. We developed a genetic algorithm for dimension selection with a distance-based fitness function for outlier detection in high dimensional data sets. This algorithm uses only a few dimensions to find the same outliers as in the whole data sets and can easily treat high dimensional data sets. The number of dimensions used being low enough, it is also possible to use visualization methods to explain and interpret outlier detection algorithm results. It is then possible to create a model from the data expert for example to qualify the detected element as an outlier or simply an error. We have also developed an evaluation measure for dimension selection in unsupervised classification and outlier detection. This measure enables us to find the same clusters as in the data set with its whole dimensions as well as clusters containing very few elements (outliers). Visual interpretation of the results shows the dimensions implied, they are considered as relevant and interesting for the clustering and outlier detection. Finally we present a semi-interactive genetic algorithm involving more significantly the user in the selection and evaluation process of the algorithm
Gueunet, Charles. "Calcul haute performance pour l'analyse topologique de données par ensembles de niveaux." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS120.
Topological Data Analysis requires efficient algorithms to deal with the continuously increasing size and level of details of data sets. In this manuscript, we focus on three fundamental topological abstractions based on level sets: merge trees, contour trees and Reeb graphs. We propose three new efficient parallel algorithms for the computation of these abstractions on multi-core shared memory workstations. The first algorithm developed in the context of this thesis is based on multi-thread parallelism for the contour tree computation. A second algorithm revisits the reference sequential algorithm to compute this abstraction and is based on local propagations expressible as parallel tasks. This new algorithm is in practice twice faster in sequential than the reference algorithm designed in 2000 and offers one order of magnitude speedups in parallel. A last algorithm also relying on task-based local propagations is presented, computing a more generic abstraction: the Reeb graph. Contrary to concurrent approaches, these methods provide the augmented version of these structures, hence enabling the full extend of level-set based analysis. Algorithms presented in this manuscript result today in the fastest implementations available to compute these abstractions. This work has been integrated into the open-source platform: the Topology Toolkit (TTK)
Ndiaye, Marie. "Exploration de grands ensembles de motifs." Thesis, Tours, 2010. http://www.theses.fr/2010TOUR4029/document.
The abundance of patterns generated by knowledge extraction algorithms is a major problem in data mining. Ta facilitate the exploration of these patterns, two approaches are often used: the first is to summarize the sets of extracted patterns and the second approach relies on the construction of visual representations of the patterns. However, the summaries are not structured and they are proposed without exploration method. Furthermore, visualizations do not provide an overview of the pattern .sets. We define a generic framework that combines the advantages of bath approaches. It allows building summaries of patterns sets at different levels of detail. These summaries provide an overview of the pattern sets and they are structured in the form of cubes on which OLAP navigational operators can be applied in order to explore the pattern sets. Moreover, we propose an algorithm which provides a summary of good quality whose size is below a given threshold. Finally, we instantiate our framework with association rules
Ould, Yahia Sabiha. "Interrogation multi-critères d'une base de données spatio-temporelles." Troyes, 2005. http://www.theses.fr/2005TROY0006.
The study of the human behavior in driving situations is of primary importance for the improvement of drivers security. This study is complex because of the numerous situations in which the driver may be involved. The objective of the CASSICE project (Symbolic Characterization of Driving Situations) is to elaborate a tool in order to simplify the analysis task of the driver's behavior. In this paper, we will mainly take an interest in the indexation and querying of a multimedia database including the numerical data and the video sequences relating to a type of driving situations. We will put the emphasis on the queries to this database. They are often complex because they are formulated according to criteria depending on time, space and they use terms of the natural language
Guerra, Thierry-Marie. "Analyse de données objectivo-subjectives : Approche par la théorie des sous-ensembles flous." Valenciennes, 1991. https://ged.uphf.fr/nuxeo/site/esupversions/a3f55508-7363-49a4-a531-9d723ff55359.
Dahabiah, Anas. "Extraction de connaissances et indexation de données multimédia pour la détection anticipée d'événements indésirables." Télécom Bretagne, 2010. http://www.theses.fr/2010TELB0117.
Similarity measuring is the essential quoin of the majority of data mining techniques and tasks in which information elements can take any type (quantities, qualitative, binary, ordinal, etc. ) and may be affected with various forms of imperfection (uncertainty, imprecision, ambiguity, etc. ). Additionally, the points of view of the experts and data owners must sometimes be considered and integrated even if presented in ambiguous or imprecise manners. Nonetheless, all the existing methods and approaches have partially handled some aspects of the aforementioned points disregarding the others. In reality, the heterogeneity, the imperfection, and the personalization have been separately conducted in prior works, using some constraints and assumptions that can overburden the procedure, limit their applications, and increase its computing time which is a crucial issue in data mining. In this thesis, we propose a novel approach essentially based on possibility theory to deal with all the aforementioned aspects within a unified general integrated framework. In order to get deeper insight and understanding of the information elements, the possibilistic modeling has been materialized via spatial, graphical and structural representations and applied to several data mining tasks using a medical database
Raschia, Guillaume. "SaintEtiq : une approche floue pour la génération de résumés à partir de bases de données relationnelles." Nantes, 2001. http://www.theses.fr/2001NANT2099.
Книги з теми "Bruit des ensembles de données":
Kohen, Dafna E. Ensembles de données nationales, sources d'information sur la garde des enfants au Canada. Ottawa: Statistique Canada, 2006.
Office statistique des Communautés européennes., ed. Développements récents dans l'analyse de grands ensembles de données: Compte rendu du séminaire ayant eu lieu à Luxembourg, les 16-18.11.1983. Luxembourg: Office des publications officielles des Communautés européennes, 1985.
RSFDGrC 2005 (2005 Regina, Sask.). Rough sets, fuzzy sets, data mining, and granular computing: 10th international conference, RSFDGrC 2005, Regina, Canada, August 31-September 3, 2005 : proceedings. Berlin: Springer, 2005.
Jian, Lirong. Hybrid rough sets and applications in uncertain decision-making. Boca Raton: Auerbach Publications, 2010.
(Editor), Dominik Slezak, Guoyin Wang (Editor), Marcin Szczuka (Editor), Ivo Duentsch (Editor), and Yiyu Yao (Editor), eds. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 10th International Conference, RSFDGrC 2005, Regina, Canada, August 31 - September 3, 2005, ... / Lecture Notes in Artificial Intelligence). Springer, 2005.
Talloen, Willem, Ziv Shkedy, Sebastian Kaiser, Sepp Hochreiter, and Adeyto Kasim. Applied Biclustering Methods for Big and High-Dimensional Data Using R. Taylor & Francis Group, 2020.
Talloen, Willem, Ziv Shkedy, Sebastian Kaiser, Adetayo Kasim, and Sepp Hochreiter. Applied Biclustering Methods for Big and High-Dimensional Data Using R. Taylor & Francis Group, 2016.
Talloen, Willem, Ziv Shkedy, Sebastian Kaiser, Adetayo Kasim, and Sepp Hochreiter. Applied Biclustering Methods for Big and High Dimensional Data Using R. Taylor & Francis Group, 2016.
Talloen, Willem, Ziv Shkedy, Sebastian Kaiser, Adetayo Kasim, and Sepp Hochreiter. Applied Biclustering Methods for Big and High-Dimensional Data Using R. Taylor & Francis Group, 2016.
Liu, Sifeng, Yi Lin, and Lirong Jian. Hybrid Rough Sets and Applications in Uncertain Decision-Making. Taylor & Francis Group, 2018.
Частини книг з теми "Bruit des ensembles de données":
Guyomard, Marc. "Ensembles de clés scalaires." In Structures de données et méthodes formelles, 147–271. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0200-8_6.
Guyomard, Marc. "Ensembles de clés structurées." In Structures de données et méthodes formelles, 273–311. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0200-8_7.
COSSART, Étienne. "Données environnementales et objets cartographiques." In Traitements et cartographie de l’information géographique, 83–106. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9161.ch3.
CHARPENTIER, Philippe. "Algorithmes en physique des particules." In Algorithmes et Société, 111–18. Editions des archives contemporaines, 2021. http://dx.doi.org/10.17184/eac.4548.
DERLUYN, Hannelore. "Observations expérimentales sur la cristallisation des sels dans les géomatériaux." In Cristallisation de sels en milieu poreux, 115–44. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9114.ch5.
ALOIRD, J., J. P. FREIERMUTH, and J. BOISSIER. "Spécificités et contraintes du transport de patients lors des missions d’évacuations médicalisées par hélicoptère de manoeuvre." In Médecine et Armées Vol. 46 No.4, 331–38. Editions des archives contemporaines, 2018. http://dx.doi.org/10.17184/eac.7322.
Aebi, Carol. "Recherches sur la Sustainability." In Recherches sur la Sustainability, 295–309. EMS Editions, 2023. http://dx.doi.org/10.3917/ems.cheva.2023.01.0295.
Dhaeze, Wouter, and Patrick Monsieur. "Le faciès céramique de la base de la classis Britannica à Boulogne-sur-Mer : présentation de deux ensembles et synthèse de données." In Boulogne-sur-Mer antique, entre terre et mer, 127–68. Presses universitaires du Septentrion, 2020. http://dx.doi.org/10.4000/books.septentrion.96322.
Fadaie, Kian, and Valerie E. Hume. "Canada Directory Information Describing Digital Geo-referenced Data Sets Information de répertoire décrivant les ensembles de données numériques à référence spatiale CAN/CGSB-171.3-95." In World Spatial Metadata Standards, 219–36. Elsevier, 2005. http://dx.doi.org/10.1016/b978-008043949-5/50015-2.
Звіти організацій з теми "Bruit des ensembles de données":
Chen, Z., S. E. Grasby, W. Yuan, M. Colpron, and X. Liu. Methodology study of geothermal resource evaluation using remote-sensing and ground-surface temperature data, Burwash Landing, Yukon – status and preliminary results. Natural Resources Canada/CMSS/Information Management, 2024. http://dx.doi.org/10.4095/p15d0hqc2g.