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Статті в журналах з теми "Analyse supervisée de graphes"
Ferraz, Antonio. "DÉTECTION À HAUTE RÉSOLUTION SPATIALE DE LA DESSERTE FORESTIÈRE EN MILIEU MONTAGNEUX." Revue Française de Photogrammétrie et de Télédétection 1, no. 211-212 (December 6, 2015): 103–17. http://dx.doi.org/10.52638/rfpt.2015.549.
Повний текст джерелаColazzo, Dario, François Goasdoué, Ionna Manolescu, and Alexandra Roatis. "Analyse de données RDF. Lentilles pour graphes sémantiques." Ingénierie des systèmes d'information 19, no. 4 (August 28, 2014): 87–117. http://dx.doi.org/10.3166/isi.19.4.87-117.
Повний текст джерелаMazille, J. E. "Analyse de structures complexes par la théorie des graphes." Revue de Métallurgie 91, no. 2 (February 1994): 223–32. http://dx.doi.org/10.1051/metal/199491020223.
Повний текст джерелаFoucambert, Denis, Tracy Heranic, Christophe Leblay, Maarit Mutta, and Minjing Zhong. "Intégration de la visualisation dans l’analyse de processus complexes : écritures et réécritures dans un corpus multilingue universitaire." SHS Web of Conferences 138 (2022): 06010. http://dx.doi.org/10.1051/shsconf/202213806010.
Повний текст джерелаGlendenning, Jonathan. "Espace disciplinaire et normativité sociale contemporaine." Perspectives étatiques 28, no. 1 (March 15, 2017): 195–210. http://dx.doi.org/10.7202/1039181ar.
Повний текст джерелаLemieux, Vincent. "L'articulation des réseaux sociaux." Recherches sociographiques 17, no. 2 (April 12, 2005): 247–60. http://dx.doi.org/10.7202/055716ar.
Повний текст джерелаBonnet, Nicolas. "Résilience d’un territoire face au chômage : les réseaux d’entreprises innovantes sur Montpellier." Nouvelles perspectives en sciences sociales 5, no. 1 (November 23, 2009): 97–115. http://dx.doi.org/10.7202/038625ar.
Повний текст джерелаHillion, H. P., and J. M. Proth. "Analyse de fabrications non linéaires et répétitives à l'aide de graphes d'événements temporisés." RAIRO - Operations Research 22, no. 2 (1988): 137–76. http://dx.doi.org/10.1051/ro/1988220201371.
Повний текст джерелаPham, Minh Tan, Grégoire Mercier, and Julien Michel. "Textural features from wavelets on graphs for very high resolution panchromatic Pléiades image classification." Revue Française de Photogrammétrie et de Télédétection, no. 208 (September 5, 2014): 131–36. http://dx.doi.org/10.52638/rfpt.2014.91.
Повний текст джерелаCHABI, Tayeb. "Identification des scenarios à la performance de l’entreprise par la productivité, rentabilité et compétitivité suivant le modèle Morphol : Cas d’un échantillon d’entreprise." Dirassat Journal Economic Issue 6, no. 1 (January 1, 2015): 291–307. http://dx.doi.org/10.34118/djei.v6i1.547.
Повний текст джерелаДисертації з теми "Analyse supervisée de graphes"
Faucheux, Cyrille. "Segmentation supervisée d'images texturées par régularisation de graphes." Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4050/document.
Повний текст джерелаIn this thesis, we improve a recent image segmentation algorithm based on a graph regularization process. The goal of this method is to compute an indicator function that satisfies a regularity and a fidelity criteria. Its particularity is to represent images with similarity graphs. This data structure allows relations to be established between similar pixels, leading to non-local processing of the data. In order to improve this approach, combine it with another non-local one: the texture features. Two solutions are developped, both based on Haralick features. In the first one, we propose a new fidelity term which is based on the work of Chan and Vese and is able to evaluate the homogeneity of texture features. In the second method, we propose to replace the fidelity criteria by the output of a supervised classifier. Trained to recognize several textures, the classifier is able to produce a better modelization of the problem by identifying the most relevant texture features. This method is also extended to multiclass segmentation problems. Both are applied to 2D and 3D textured images
Fontaine, Michaël. "Segmentation non supervisée d'images couleur par analyse de la connexité des pixels." Lille 1, 2001. https://pepite-depot.univ-lille.fr/LIBRE/Th_Num/2001/50376-2001-305-306.pdf.
Повний текст джерелаRibeyre, Corentin. "Méthodes d’analyse supervisée pour l’interface syntaxe-sémantique : de la réécriture de graphes à l’analyse par transitions." Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCC119.
Повний текст джерелаNowadays, the amount of textual data has become so gigantic, that it is not possible to deal with it manually. In fact, it is now necessary to use Natural Language Processing techniques to extract useful information from these data and understand their underlying meaning. In this thesis, we offer resources, models and methods to allow: (i) the automatic annotation of deep syntactic corpora to extract argument structure that links (verbal) predicates to their arguments (ii) the use of these resources with the help of efficient methods. First, we develop a graph rewriting system and a set of manually-designed rewriting rules to automatically annotate deep syntax in French. Thanks to this approach, two corpora were created: the DeepSequoia, a deep syntactic version of the Séquoia corpus and the DeepFTB, a deep syntactic version of the dependency version of the French Treebank. Next, we extend two transition-based parsers and adapt them to be able to deal with graph structures. We also develop a set of rich linguistic features extracted from various syntactic trees. We think they are useful to bring different kind of topological information to accurately predict predicat-argument structures. Used in an arc-factored second-order parsing model, this set of features gives the first state-of-the-art results on French and outperforms the one established on the DM and PAS corpora for English. Finally, we briefly explore a method to automatically induce the transformation between a tree and a graph. This completes our set of coherent resources and models to automatically analyze the syntax-semantics interface on French and English
Pujari, Manisha. "Prévision de liens dans des grands graphes de terrain (application aux réseaux bibliographiques)." Thesis, Sorbonne Paris Cité, 2015. http://www.theses.fr/2015USPCD010/document.
Повний текст джерелаIn this work, we are interested to tackle the problem of link prediction in complex networks. In particular, we explore topological dyadic approaches for link prediction. Different topological proximity measures have been studied in the scientific literature for finding the probability of appearance of new links in a complex network. Supervided learning methods have also been used to combine the predictions made or information provided by different topological measures. The create predictive models using various topological measures. The problem of supervised learning for link prediction is a difficult problem especially due to the presence of heavy class imbalance. In this thesis, we search different alternative approaches to improve the performance of different dyadic approaches for link prediction. We propose here, a new approach of link prediction based on supervised rank agregation that uses concepts from computational social choice theory. Our approach is founded on supervised techniques of aggregating sorted lists (or preference aggregation). We also explore different ways of improving supervised link prediction approaches. One approach is to extend the set of attributes describing an example (pair of nodes) by attributes calculated in a multiplex network that includes the target network. Multiplex networks have a layered structure, each layer having different kinds of links between same sets of nodes. The second way is to use community information for sampling of examples to deal with the problem of classe imabalance. Experiments conducted on real networks extracted from well known DBLP bibliographic database
Sevi, Harry. "Analyse harmonique sur graphes dirigés et applications : de l'analyse de Fourier aux ondelettes." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEN068/document.
Повний текст джерелаThe research conducted in this thesis aims to develop a harmonic analysis for functions defined on the vertices of an oriented graph. In the era of data deluge, much data is in the form of graphs and data on this graph. In order to analyze and exploit this graph data, we need to develop mathematical and numerically efficient methods. This development has led to the emergence of a new theoretical framework called signal processing on graphs, which aims to extend the fundamental concepts of conventional signal processing to graphs. Inspired by the multi-scale aspect of graphs and graph data, many multi-scale constructions have been proposed. However, they apply only to the non-directed framework. The extension of a harmonic analysis on an oriented graph, although natural, is complex. We, therefore, propose a harmonic analysis using the random walk operator as the starting point for our framework. First, we propose Fourier-type bases formed by the eigenvectors of the random walk operator. From these Fourier bases, we determine a frequency notion by analyzing the variation of its eigenvectors. The determination of a frequency analysis from the basis of the vectors of the random walk operator leads us to multi-scale constructions on oriented graphs. More specifically, we propose a wavelet frame construction as well as a decimated wavelet construction on directed graphs. We illustrate our harmonic analysis with various examples to show its efficiency and relevance
Galluccio, Laurent. "Analyse et segmentation de données non supervisées à l'aide de graphe." Nice, 2010. http://www.theses.fr/2010NICE4022.
Повний текст джерелаThis thesis presents new data segmentation and data clustering methods applied to astrophysical data. A priori information such as the number of classes or the underlying data distribution is not necessarily known. Many classification methods in astrophysics community are based on a priori knowledges or on observations already realized on data. Classifications obtained will depend on these information and will be limited by the experts knowledge. The goal of developing clustering algorithms is to get rid of these limitations, to be able to potentially detect new classes. The main approach chosen in this thesis is the use of a graph built on the data : the Minimal Spanning Tree (MST). By connecting the points by segments we build a structure which encapsulates the being relations between each pair of points. We propose a method to estimate both the number and the position of clusters by exploring the connections of the MST built. A data partition is obtained by using this information to initialize some clustering algorithms. A new class of multi-rooted MSTs is introduced. From their construction, new distance measures are derived allowing to take into account both the local and global data neighborhood. A clustering method which combines results of multiple partitionments realized on the multi-rooted trees is also exposed. The methods proposed are validated on benchmarks and applied to astrophysical datasets
Fontaine, Michaël Macaire Ludovic Postaire Jack-Gérard. "Segmentation non supervisée d'images couleur par analyse de la connexité des pixels." [S.l.] : [s.n.], 2001. http://www.univ-lille1.fr/bustl-grisemine/pdf/extheses/50376-2001-305-306.pdf.
Повний текст джерелаGaillard, Pierre. "Apprentissage statistique de la connexité d'un nuage de points par modèle génératif : application à l'analyse exploratoire et la classification semi-supervisée." Compiègne, 2008. http://www.theses.fr/2008COMP1767.
Повний текст джерелаIn this work, we propose a statistical model to learn the connectedness of a set of points. This model combine geometrical and statistical approaches by defining a mixture model based on a graph. From this generative graph, we propose and evaluate methods and algorithms to analyse the set of points and to realize semi-supervised learning
Gan, Changquan. "Une approche de classification non supervisée basée sur la notion des K plus proches voisins." Compiègne, 1994. http://www.theses.fr/1994COMP765S.
Повний текст джерелаCorneli, Marco. "Dynamic stochastic block models, clustering and segmentation in dynamic graphs." Thesis, Paris 1, 2017. http://www.theses.fr/2017PA01E012/document.
Повний текст джерелаThis thesis focuses on the statistical analysis of dynamic graphs, both defined in discrete or continuous time. We introduce a new extension of the stochastic block model (SBM) for dynamic graphs. The proposed approach, called dSBM, adopts non homogeneous Poisson processes to model the interaction times between pairs of nodes in dynamic graphs, either in discrete or continuous time. The intensity functions of the processes only depend on the node clusters, in a block modelling perspective. Moreover, all the intensity functions share some regularity properties on hidden time intervals that need to be estimated. A recent estimation algorithm for SBM, based on the greedy maximization of an exact criterion (exact ICL) is adopted for inference and model selection in dSBM. Moreover, an exact algorithm for change point detection in time series, the "pruned exact linear time" (PELT) method is extended to deal with dynamic graph data modelled via dSBM. The approach we propose can be used for change point analysis in graph data. Finally, a further extension of dSBM is developed to analyse dynamic net- works with textual edges (like social networks, for instance). In this context, the graph edges are associated with documents exchanged between the corresponding vertices. The textual content of the documents can provide additional information about the dynamic graph topological structure. The new model we propose is called "dynamic stochastic topic block model" (dSTBM).Graphs are mathematical structures very suitable to model interactions between objects or actors of interest. Several real networks such as communication networks, financial transaction networks, mobile telephone networks and social networks (Facebook, Linkedin, etc.) can be modelled via graphs. When observing a network, the time variable comes into play in two different ways: we can study the time dates at which the interactions occur and/or the interaction time spans. This thesis only focuses on the first time dimension and each interaction is assumed to be instantaneous, for simplicity. Hence, the network evolution is given by the interaction time dates only. In this framework, graphs can be used in two different ways to model networks. Discrete time […] Continuous time […]. In this thesis both these perspectives are adopted, alternatively. We consider new unsupervised methods to cluster the vertices of a graph into groups of homogeneous connection profiles. In this manuscript, the node groups are assumed to be time invariant to avoid possible identifiability issues. Moreover, the approaches that we propose aim to detect structural changes in the way the node clusters interact with each other. The building block of this thesis is the stochastic block model (SBM), a probabilistic approach initially used in social sciences. The standard SBM assumes that the nodes of a graph belong to hidden (disjoint) clusters and that the probability of observing an edge between two nodes only depends on their clusters. Since no further assumption is made on the connection probabilities, SBM is a very flexible model able to detect different network topologies (hubs, stars, communities, etc.)
Книги з теми "Analyse supervisée de graphes"
Whittaker, J. Graphical models in applied multivariate statistics. Chichester [England]: Wiley, 1990.
Знайти повний текст джерелаClassification and regression trees. New York, N.Y: Chapman & Hall, 1993.
Знайти повний текст джерелаCombinatorial algorithms. Bristol: Adam Hilger, 1990.
Знайти повний текст джерелаKučera, Luděk. Combinatorial algorithms. Bristol: Adam Hilger, 1989.
Знайти повний текст джерела(Foreword), L. Accardi, ed. Quantum Probability and Spectral Analysis of Graphs (Theoretical and Mathematical Physics). Springer, 2007.
Знайти повний текст джерелаBalakrishnan, R., and Xuding Zhu. Combinatorial Nullstellensatz: With Applications to Graph Colouring. Taylor & Francis Group, 2021.
Знайти повний текст джерелаBalakrishnan, R., and Xuding Zhu. Combinatorial Nullstellensatz: With Applications to Graph Colouring. Taylor & Francis Group, 2021.
Знайти повний текст джерелаHora, Akihito, L. Accardi, and Nobuaki Obata. Quantum Probability and Spectral Analysis of Graphs. Springer London, Limited, 2007.
Знайти повний текст джерелаЧастини книг з теми "Analyse supervisée de graphes"
Suso, Albert, Pau Riba, Oriol Ramos Terrades, and Josep Lladós. "A Self-supervised Inverse Graphics Approach for Sketch Parametrization." In Document Analysis and Recognition – ICDAR 2021 Workshops, 28–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86198-8_3.
Повний текст джерелаDu, Jiangshan, Liping Zheng, and Jun Shi. "Graph Embedding Discriminant Analysis and Semi-Supervised Extension for Face Recognition." In Image and Graphics Technologies and Applications, 57–73. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-6033-4_5.
Повний текст джерелаde Vriendt, Marianne, Philip Sellars, and Angelica I. Aviles-Rivero. "The GraphNet Zoo: An All-in-One Graph Based Deep Semi-supervised Framework for Medical Image Classification." In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, 187–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60365-6_18.
Повний текст джерелаDiago Ferrer, L. "An Analysis of Supervised Practical Work as a Didactic Methodology in the Subject of Graphic Expression in Engineering." In Advances on Mechanics, Design Engineering and Manufacturing II, 722–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12346-8_70.
Повний текст джерелаLIU, Sicong, Francesca BOVOLO, Lorenzo BRUZZONE, Qian DU, and Xiaohua TONG. "Détection non supervisée des changements dans des images multitemporelles." In Détection de changements et analyse des séries temporelles d’images 1, 5–40. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9056.ch1.
Повний текст джерелаATTO, Abdourrahmane M., Fatima KARBOU, Sophie GIFFARD-ROISIN, and Lionel BOMBRUN. "Clustering fonctionnel de séries d’images par entropies relatives." In Détection de changements et analyse des séries temporelles d’images 1, 121–38. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9056.ch4.
Повний текст джерела"Supervised Classification." In Interactive and Dynamic Graphics for Data Analysis, 63–101. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-71762-3_4.
Повний текст джерелаHai-Jew, Shalin. "Applied Analytical “Distant Reading” using NVivo 11 Plus™." In Social Media Data Extraction and Content Analysis, 159–201. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0648-5.ch007.
Повний текст джерелаPHAM, Minh-Tan, and Grégoire MERCIER. "Détection de changements sur les graphes de séries SAR." In Détection de changements et analyse des séries temporelles d’images 1, 183–219. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9056.ch7.
Повний текст джерелаXu, Tiange, and Fu Zhang. "A Brief Review of Relation Extraction Based on Pre-Trained Language Models." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200755.
Повний текст джерелаТези доповідей конференцій з теми "Analyse supervisée de graphes"
Huang, Zhichao, Xutao Li, Yunming Ye, and Michael K. Ng. "MR-GCN: Multi-Relational Graph Convolutional Networks based on Generalized Tensor Product." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/175.
Повний текст джерелаJu, Wei, Xiao Luo, Meng Qu, Yifan Wang, Chong Chen, Minghua Deng, Xian-Sheng Hua, and Ming Zhang. "TGNN: A Joint Semi-supervised Framework for Graph-level Classification." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/295.
Повний текст джерелаLi, Jian, Yong Liu, Rong Yin, and Weiping Wang. "Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/400.
Повний текст джерелаFei Wang, Xin Wang, and Tao Li. "Beyond the graphs: Semi-parametric semi-supervised discriminant analysis." In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009. http://dx.doi.org/10.1109/cvprw.2009.5206675.
Повний текст джерелаWang, Fei, Xin Wang, and Tao Li. "Beyond the graphs: Semi-parametric semi-supervised discriminant analysis." In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2009. http://dx.doi.org/10.1109/cvpr.2009.5206675.
Повний текст джерелаGao, Haoyuan, Liansheng Zhuang, and Nenghai Yu. "A New Graph Constructor for Semi-supervised Discriminant Analysis via Group Sparsity." In Graphics (ICIG). IEEE, 2011. http://dx.doi.org/10.1109/icig.2011.82.
Повний текст джерелаBenato, Bárbara C., Alexandru C. Telea, and Alexandre X. Falcão. "Semi-Automatic Data Annotation guided by Feature Space Projection." In Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sibgrapi.est.2020.12976.
Повний текст джерелаDos Santos, Fernando Pereira, and Moacir Antonelli Ponti. "Features transfer learning for image and video recognition tasks." In Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sibgrapi.est.2020.12980.
Повний текст джерелаTian, Qingwen, Shixing Zhou, Yu Cheng, Jianxia Chen, Yi Gao, and Shuijing Zhang. "Curriculum Semantic Retrieval System based on Distant Supervision." In 7th International Conference on Software Engineering and Applications (SOFEA 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111603.
Повний текст джерелаKan, Kevin C., and Greg A. Jamieson. "Ecological Interface Design for a Water Monitoring Decision Aid." In ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/esda2012-82648.
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