Literatura científica selecionada sobre o tema "Apprentissage automatique non supervisée"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Apprentissage automatique non supervisée".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "Apprentissage automatique non supervisée"
Jacopin, Eliott, Antoine Cornuéjols, Christine Martin, Farzaneh Kazemipour e Christophe Sausse. "Détection automatique de plantes au sein d’images aériennes de champs par apprentissage non supervisé et approche multi-agents". Revue Ouverte d'Intelligence Artificielle 2, n.º 1 (17 de novembro de 2021): 123–56. http://dx.doi.org/10.5802/roia.12.
Texto completo da fonteHeddam, Salim, Abdelmalek Bermad e Noureddine Dechemi. "Modélisation de la dose de coagulant par les systèmes à base d’inférence floue (ANFIS) application à la station de traitement des eaux de Boudouaou (Algérie)". Revue des sciences de l’eau 25, n.º 1 (28 de março de 2012): 1–17. http://dx.doi.org/10.7202/1008532ar.
Texto completo da fonteChehata, Nesrine, Karim Ghariani, Arnaud Le Bris e Philippe Lagacherie. "Apport des images pléiades pour la délimitation des parcelles agricoles à grande échelle". Revue Française de Photogrammétrie et de Télédétection, n.º 209 (29 de janeiro de 2015): 165–71. http://dx.doi.org/10.52638/rfpt.2015.220.
Texto completo da fonteSbihi, Mohammed, Ahmed Moussa, Jack-Gérard Postaire e Abderrahmane Sbihi. "Approche markovienne pour la classification automatique non supervisée de données multidimensionnelles". Journal Européen des Systèmes Automatisés 39, n.º 9-10 (30 de dezembro de 2005): 1133–54. http://dx.doi.org/10.3166/jesa.39.1133-1154.
Texto completo da fonteBenmostefa, Soumia, e Hadria Fizazi. "Classification automatique des images satellitaires optimisée par l'algorithme des chauves-souris". Revue Française de Photogrammétrie et de Télédétection, n.º 203 (8 de abril de 2014): 11–17. http://dx.doi.org/10.52638/rfpt.2013.25.
Texto completo da fonteMANDEL, P., A. FLEURY, K. DELABRE e V. HEIM. "La conductivité électrique, témoin opérationnel de la qualité de l’eau dans un réseau de distribution". Techniques Sciences Méthodes 11 (21 de novembro de 2022): 27–37. http://dx.doi.org/10.36904/tsm/202211027.
Texto completo da fonteForestier, Michèle. "De la naissance aux premiers pas". Thérapie Psychomotrice et Recherches N° 187, n.º 3 (1 de julho de 2022): 36–42. http://dx.doi.org/10.3917/tpr.187.0036.
Texto completo da fonteOhmaid, Hicham, S. Eddarouich, A. Bourouhou e M. Timouya. "Comparison between SVM and KNN classifiers for iris recognition using a new unsupervised neural approach in segmentation". IAES International Journal of Artificial Intelligence (IJ-AI) 9, n.º 3 (1 de setembro de 2020): 429. http://dx.doi.org/10.11591/ijai.v9.i3.pp429-438.
Texto completo da fonteTeses / dissertações sobre o assunto "Apprentissage automatique non supervisée"
Delsert, Stéphane. "Classification interactive non supervisée de données multidimensionnelles par réseaux de neurones à apprentissage cométitif". Lille 1, 1996. https://pepite-depot.univ-lille.fr/LIBRE/Th_Num/1996/50376-1996-214.pdf.
Texto completo da fonteGuérif, Sébastien. "Réduction de dimension en apprentissage numérique non supervisé". Paris 13, 2006. http://www.theses.fr/2006PA132032.
Texto completo da fontePeyrache, Jean-Philippe. "Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée". Thesis, Saint-Etienne, 2014. http://www.theses.fr/2014STET4023/document.
Texto completo da fonteDuring the past few years, an increasing interest for Machine Learning has been encountered, in various domains like image recognition or medical data analysis. However, a limitation of the classical PAC framework has recently been highlighted. It led to the emergence of a new research axis: Domain Adaptation (DA), in which learning data are considered as coming from a distribution (the source one) different from the one (the target one) from which are generated test data. The first theoretical works concluded that a good performance on the target domain can be obtained by minimizing in the same time the source error and a divergence term between the two distributions. Three main categories of approaches are derived from this idea : by reweighting, by reprojection and by self-labeling. In this thesis work, we propose two contributions. The first one is a reprojection approach based on boosting theory and designed for numerical data. It offers interesting theoretical guarantees and also seems able to obtain good generalization performances. Our second contribution consists first in a framework filling the gap of the lack of theoretical results for self-labeling methods by introducing necessary conditions ensuring the good behavior of this kind of algorithm. On the other hand, we propose in this framework a new approach, using the theory of (epsilon, gamma, tau)- good similarity functions to go around the limitations due to the use of kernel theory in the specific context of structured data
Cleuziou, Guillaume. "Une méthode de classification non-supervisée pour l'apprentissage de règles et la recherche d'information". Phd thesis, Université d'Orléans, 2004. http://tel.archives-ouvertes.fr/tel-00084828.
Texto completo da fonteNous proposons, dans cette étude, l'algorithme de clustering PoBOC permettant de structurer un ensemble d'objets en classes non-disjointes. Nous utilisons cette méthode de clustering comme outil de traitement dans deux applications très différentes.
- En apprentissage supervisé, l'organisation préalable des instances apporte une connaissance utile pour la tâche d'induction de règles propositionnelles et logiques.
- En Recherche d'Information, les ambiguïtés et subtilités de la langue naturelle induisent naturellement des recouvrements entre thématiques.
Dans ces deux domaines de recherche, l'intérêt d'organiser les objets en classes non-disjointes est confirmé par les études expérimentales adaptées.
Fischer, Aurélie. "Apprentissage statistique non supervisé : grande dimension et courbes principales". Paris 6, 2011. http://www.theses.fr/2011PA066142.
Texto completo da fonteRibeiro, Swen. "Induction non-supervisée de schémas d’évènements à partir de textes journalistiques". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASS059.
Texto completo da fonteEvents are central in many Natural Language Processing tasks, despite the lack of a unified definition for the concept. The field of event processing took off with the MUC evaluation campaigns that provided participants with reference structures called templates. These templates were composed of a title (the name of the event) and several slots, i.e specific and atomic pieces of data about the event. Creating these templates is an expert task and therefore costly, painstaking and hard to extend to new domains.Meanwhile, the amount of data produced by individuals and organizations has grown exponentially, opening unprecedented perspectives of applications. In the journalistic domain, it fueled the development of a new paradigm called data-journalism.In this work, we aim at inducing synthetic representations of events from large textual journalistic corpora. These representations would be comparable to MUC templates and used by data-journalists to explore large textual news datasets. To this end, we propose a bottom-up approach composed of three main steps. The first step clusters several textual mentions of a same particular event (i.e tied to a time and place) to identify distinct instances. The second step groups these instances together based on more abstract features to infer event types. Finally, the third and last step extracts the most salient elements of each type to produce the synthetic, template-like structure we are looking for
Sublemontier, Jacques-Henri. "Classification non supervisée : de la multiplicité des données à la multiplicité des analyses". Phd thesis, Université d'Orléans, 2012. http://tel.archives-ouvertes.fr/tel-00801555.
Texto completo da fonteBach, Tran. "Algorithmes avancés de DCA pour certaines classes de problèmes en apprentissage automatique du Big Data". Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0255.
Texto completo da fonteBig Data has become gradually essential and ubiquitous in all aspects nowadays. Therefore, there is an urge to develop innovative and efficient techniques to deal with the rapid growth in the volume of data. This dissertation considers the following problems in Big Data: group variable selection in multi-class logistic regression, dimension reduction by t-SNE (t-distributed Stochastic Neighbor Embedding), and deep clustering. We develop advanced DCAs (Difference of Convex functions Algorithms) for these problems, which are based on DC Programming and DCA – the powerful tools for non-smooth non-convex optimization problems. Firstly, we consider the problem of group variable selection in multi-class logistic regression. We tackle this problem by using recently advanced DCAs -- Stochastic DCA and DCA-Like. Specifically, Stochastic DCA specializes in the large sum of DC functions minimization problem, which only requires a subset of DC functions at each iteration. DCA-Like relaxes the convexity condition of the second DC component while guaranteeing the convergence. Accelerated DCA-Like incorporates the Nesterov's acceleration technique into DCA-Like to improve its performance. The numerical experiments in benchmark high-dimensional datasets show the effectiveness of proposed algorithms in terms of running time and solution quality. The second part studies the t-SNE problem, an effective non-linear dimensional reduction technique. Motivated by the novelty of DCA-Like and Accelerated DCA-Like, we develop two algorithms for the t-SNE problem. The superiority of proposed algorithms in comparison with existing methods is illustrated through numerical experiments for visualization application. Finally, the third part considers the problem of deep clustering. In the first application, we propose two algorithms based on DCA to combine t-SNE with MSSC (Minimum Sum-of-Squares Clustering) by following two approaches: “tandem analysis” and joint-clustering. The second application considers clustering with auto-encoder (a well-known type of neural network). We propose an extension to a class of joint-clustering algorithms to overcome the scaling problem and applied for a specific case of joint-clustering with MSSC. Numerical experiments on several real-world datasets show the effectiveness of our methods in rapidity and clustering quality, compared to the state-of-the-art methods
Martel-Brisson, Nicolas. "Approche non supervisée de segmentation de bas niveau dans un cadre de surveillance vidéo d'environnements non contrôlés". Thesis, Université Laval, 2012. http://www.theses.ulaval.ca/2012/29093/29093.pdf.
Texto completo da fonteSîrbu, Adela-Maria. "Dynamic machine learning for supervised and unsupervised classification". Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0002/document.
Texto completo da fonteThe research direction we are focusing on in the thesis is applying dynamic machine learning models to salve supervised and unsupervised classification problems. We are living in a dynamic environment, where data is continuously changing and the need to obtain a fast and accurate solution to our problems has become a real necessity. The particular problems that we have decided te approach in the thesis are pedestrian recognition (a supervised classification problem) and clustering of gene expression data (an unsupervised classification. problem). The approached problems are representative for the two main types of classification and are very challenging, having a great importance in real life.The first research direction that we approach in the field of dynamic unsupervised classification is the problem of dynamic clustering of gene expression data. Gene expression represents the process by which the information from a gene is converted into functional gene products: proteins or RNA having different roles in the life of a cell. Modern microarray technology is nowadays used to experimentally detect the levels of expressions of thousand of genes, across different conditions and over time. Once the gene expression data has been gathered, the next step is to analyze it and extract useful biological information. One of the most popular algorithms dealing with the analysis of gene expression data is clustering, which involves partitioning a certain data set in groups, where the components of each group are similar to each other. In the case of gene expression data sets, each gene is represented by its expression values (features), at distinct points in time, under the monitored conditions. The process of gene clustering is at the foundation of genomic studies that aim to analyze the functions of genes because it is assumed that genes that are similar in their expression levels are also relatively similar in terms of biological function.The problem that we address within the dynamic unsupervised classification research direction is the dynamic clustering of gene expression data. In our case, the term dynamic indicates that the data set is not static, but it is subject to change. Still, as opposed to the incremental approaches from the literature, where the data set is enriched with new genes (instances) during the clustering process, our approaches tackle the cases when new features (expression levels for new points in time) are added to the genes already existing in the data set. To our best knowledge, there are no approaches in the literature that deal with the problem of dynamic clustering of gene expression data, defined as above. In this context we introduced three dynamic clustering algorithms which are able to handle new collected gene expression levels, by starting from a previous obtained partition, without the need to re-run the algorithm from scratch. Experimental evaluation shows that our method is faster and more accurate than applying the clustering algorithm from scratch on the feature extended data set
Capítulos de livros sobre o assunto "Apprentissage automatique non supervisée"
OUVRARD 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.
Texto completo da fonte