Literatura científica selecionada sobre o tema "Réseaux de neurones bayésiens"
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Artigos de revistas sobre o assunto "Réseaux de neurones bayésiens"
Mallet, L. "∑njeux de la πsychiatrie ℂomputationnelle". European Psychiatry 30, S2 (novembro de 2015): S50—S51. http://dx.doi.org/10.1016/j.eurpsy.2015.09.143.
Texto completo da fonteNohair, Mohamed, André St-Hilaire e Taha B. Ouarda. "Utilisation des réseaux de neurones et de la régularisation bayésienne en modélisation de la température de l’eau en rivière". Revue des sciences de l'eau 21, n.º 3 (2 de outubro de 2008): 373–82. http://dx.doi.org/10.7202/018783ar.
Texto completo da fonteGonzales, Christophe, e Pierre-Henri Wuillemin. "Réseaux bayésiens en modélisation d'utilisateurs." Sciences et techniques éducatives 5, n.º 2 (1998): 173–98. http://dx.doi.org/10.3406/stice.1998.1384.
Texto completo da fonteNohair, Mohamed, Ouafae Britel, Nabil Souaf, Driss Zakarya, Abdelmjid Hafid e Noura Mallouk. "Application Des Réseaux de Neurones Avec la Régularisation Bayésienne pour la Modélisation de la Synthèse de l’hydroxyapatite Élaborée à Partir du Carbonate de Calcium et de L’acide Phosphorique". Phosphorus, Sulfur, and Silicon and the Related Elements 185, n.º 8 (30 de julho de 2010): 1772–81. http://dx.doi.org/10.1080/10426500903299877.
Texto completo da fonteCondamin, Laurent, e Patrick Naïm. "Analyse des risques opérationnels par les réseaux bayésiens". Revue d'économie financière 84, n.º 3 (2006): 121–46. http://dx.doi.org/10.3406/ecofi.2006.4122.
Texto completo da fonteApedome, Kouami Seli, Sid-Ali Addouche, Méziane Bennour, David Tchoffa e Abderrahman El-Mhamedi. "Proposition d’une démarche de formalisation d’expérience par les réseaux bayésiens". Journal Européen des Systèmes Automatisés 46, n.º 8 (30 de dezembro de 2012): 809–33. http://dx.doi.org/10.3166/jesa.46.809-833.
Texto completo da fonteWeber, Philippe, e Marie-Christine Suhner. "Modélisation de processus industriels par réseaux bayésiens orientés objet (RBOO)". Revue d'intelligence artificielle 18, n.º 2 (1 de abril de 2004): 299–326. http://dx.doi.org/10.3166/ria.18.299-326.
Texto completo da fonte-BORNE, Pierre. "Les réseaux de neurones." Revue de l'Electricité et de l'Electronique -, n.º 08 (2006): 31. http://dx.doi.org/10.3845/ree.2006.074.
Texto completo da fonte-BORNE, Pierre. "Les réseaux de neurones." Revue de l'Electricité et de l'Electronique -, n.º 08 (2006): 37. http://dx.doi.org/10.3845/ree.2006.075.
Texto completo da fonte-Y. HAGGEGE, Joseph. "Les réseaux de neurones". Revue de l'Electricité et de l'Electronique -, n.º 08 (2006): 43. http://dx.doi.org/10.3845/ree.2006.076.
Texto completo da fonteTeses / dissertações sobre o assunto "Réseaux de neurones bayésiens"
Abid, Ilyes. "Modélisation de la prévision de défaillance des entreprises par des approches statiques et dynamiques : réseaux de neurones, réseaux bayésiens, modèles de durée et dichotomiques". Thesis, Paris 10, 2011. http://www.theses.fr/2011PA100151.
Texto completo da fonteThe objective of this thesis is to study bankruptcy prediction models from both static and dynamic viewpoints. More precisely, in the static approach, we used the methods of selecting discriminating variables using the neural networks. We thus proposed two new procedures relating to these methods. The first one is based on the criterion HVS called HVS-AUC and allowed to 1) build a more parsimonious model compared to the LDA, 2) identify a set of variables both static and non-cyclical with a strong explanatory power. Conversely, the second technique is based on the forward procedure, more precisely on forward-AUC. This method shows results comparable to the LDA but with fewer variables. It allows the detection of ratios considered as the most relevant according to LDA and HVS-AUC. We have also used methods of structure learning of Bayesian networks to improve the performance of classification of firms. We have mobilized a technique called "Max-Min Hill-Climbing" or MMHC. Specifically, we plan to analyze the performance of classification of an algorithm that mixes both MMHC and the canonical model of a naive Bayes network (NB). This new method could be called NB-MMHC (naive Bayes augmented by MMH C). The results confirm the prevailing view: as for the discriminatory power, no structure seems to be able to significantly compete with NB. In the second dynamic approach, we put more emphasis on factors not measurable a priori and also on explanatory factors impossible to capture within a static framework. In the first phase, we used the macroeconomic variables to better estimate the risk of default. In the second part, we used an alternative model to better estimate the shocks that firms could undergo over time. We therefore evaluate the propagation effects of theses shocks
Bourgeois, Yoann. "Les réseaux de neurones artificiels pour mesurer les risques économiques et financiers". Paris, EHESS, 2003. http://www.theses.fr/2003EHES0118.
Texto completo da fonteThe objective of this thesis is to provide complete methodologies to solve prediction and classification problems in economy and finance by using Artificial Neural networks. The plan of work shows that the thesisplays a great part in establishing in several ways a statistic methodology for neural networks. We proceed in four chapters. The first chapter describes supervised and unsupervised neural network methodology to modelize quantitative or qualitative variables. In the second chapter, we are interested by the bayesian approach for supervised neural networks and the developpement of a set of misspecification statistic tests for binary choice models. In chapter three, we show that multivariate supervised neural networks enable to take into account structural changes and the neural networks methodology is able to estimate some probabilities of exchange crisis. In chapter four, we develope a complete based neural network-GARCH model to manage a stocks portfolio. We introduce some terms as conditional returns or conditional risk for a stock or a portfolio. Next, we apply bayesian Self-Organizing Map in order to estimate the univariate probability density function of the DM/USD exchange rate
Mallet, Grégory. "Méthodes statistiques pour la prédiction de température dans les composants hyperfréquences". Phd thesis, INSA de Rouen, 2010. http://tel.archives-ouvertes.fr/tel-00586089.
Texto completo da fonteLiu, Haoran. "Statistical and intelligent methods for default diagnosis and loacalization in a continuous tubular reactor". Phd thesis, INSA de Rouen, 2009. http://tel.archives-ouvertes.fr/tel-00560886.
Texto completo da fonteTchoumatchenko, Irina. "Extraction des règles logiques dans des réseaux de neurones formels : application a la prédiction de la structure secondaire des protéines". Paris 6, 1994. http://www.theses.fr/1994PA066448.
Texto completo da fonteKozyrskiy, Bogdan. "Exploring the Intersection of Bayesian Deep Learning and Gaussian Processes". Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS064archi.pdf.
Texto completo da fonteDeep learning played a significant role in establishing machine learning as a must-have instrument in multiple areas. The use of deep learning poses several challenges. Deep learning requires a lot of computational power for training and applying models. Another problem with deep learning is its inability to estimate the uncertainty of the predictions, which creates obstacles in risk-sensitive applications. This thesis presents four projects to address these problems: We propose an approach making use of Optical Processing Units to reduce energy consumption and speed up the inference of deep models. We address the problem of uncertainty estimates for classification with Bayesian inference. We introduce techniques for deep models that decreases the cost of Bayesian inference. We developed a novel framework to accelerate Gaussian Process regression. We propose a technique to impose meaningful functional priors for deep models through Gaussian Processes
Kacimi, Sahra. "Contribution à la restitution des précipitations tropicales par radiométrie micro-ondes : préparation à la mission Megha-Tropiques". Versailles-St Quentin en Yvelines, 2012. http://www.theses.fr/2012VERS0064.
Texto completo da fonteWithin the framework of the global warming, the analysis of water and energy budget is of major importance. Considering the Megha-Tropiques (MT) mission whose one of the scientific objectives is to improve the knowledge of water and energy cycle in the intertropical region, the estimation of instantaneous surface rainfall is of the great importance. My PhD work focuses on the optimization of a multi-region, the estimation of instantaneous surface rainfall is of great importance. My PhD work focuses on the optimization of a multi-plateform Bayesian retrieval algorithm called BRAIN (Bayesian Retrieval Algorithm Including Neural Networks) (Viltard et al. , 2006) used for MT. This algorithm uses passive microwave data from satellites such as TRMM, SSM/I and AQUA. It uses a Bayesian Monte Carlo approach to retrieve several atmospheric parameters such as the instantaneous rainfall rate. In order to get a more accurate rainfall restitution, two research axes were investigated : the detection of a priori rainy areas that takes place before the rainfall estimation itself, and the impact of the database and inversion parameters. First, the database on which the algorithm relies needs to be more representative especially as far as high rain rates are concerned. To improve the representativeness of the inversion database, we need first to eliminate repetitive profiles, that is to say extract prototypes from it. To be made, we use Self Organizing Maps SO%s developed by T. Kohonen (2001). Second, the improvement of the rainy-non-rainy pixels classification before the inversion was made using neural networks
Tran, Gia-Lac. "Advances in Deep Gaussian Processes : calibration and sparsification". Electronic Thesis or Diss., Sorbonne université, 2020. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2020SORUS410.pdf.
Texto completo da fonteGaussian Processes (GPs) are an attractive specific way of doing non-parametric Bayesian modeling in a supervised learning problem. It is well-known that GPs are able to make inferences as well as predictive uncertainties with a firm mathematical background. However, GPs are often unfavorable by the practitioners due to their kernel's expressiveness and the computational requirements. Integration of (convolutional) neural networks and GPs are a promising solution to enhance the representational power. As our first contribution, we empirically show that these combinations are miscalibrated, which leads to over-confident predictions. We also propose a novel well-calibrated solution to merge neural structures and GPs by using random features and variational inference techniques. In addition, these frameworks can be intuitively extended to reduce the computational cost by using structural random features. In terms of computational cost, the exact Gaussian Processes require the cubic complexity to training size. Inducing point-based Gaussian Processes are a common choice to mitigate the bottleneck by selecting a small set of active points through a global distillation from available observations. However, the general case remains elusive and it is still possible that the required number of active points may exceed a certain computational budget. In our second study, we propose Sparse-within-Sparse Gaussian Processes which enable the approximation with a large number of inducing points without suffering a prohibitive computational cost
Wolinski, Pierre. "Structural Learning of Neural Networks". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASS026.
Texto completo da fonteThe structure of a neural network determines to a large extent its cost of training and use, as well as its ability to learn. These two aspects are usually in competition: the larger a neural network is, the better it will perform the task assigned to it, but the more it will require memory and computing time resources for training. Automating the search of efficient network structures -of reasonable size and performing well- is then a very studied question in this area. Within this context, neural networks with various structures are trained, which requires a new set of training hyperparameters for each new structure tested. The aim of the thesis is to address different aspects of this problem. The first contribution is a training method that operates within a large perimeter of network structures and tasks, without needing to adjust the learning rate. The second contribution is a network training and pruning technique, designed to be insensitive to the initial width of the network. The last contribution is mainly a theorem that makes possible to translate an empirical training penalty into a Bayesian prior, theoretically well founded. This work results from a search for properties that theoretically must be verified by training and pruning algorithms to be valid over a wide range of neural networks and objectives
Fond, Antoine. "Localisation par l'image en milieu urbain : application à la réalité augmentée". Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0028/document.
Texto completo da fonteThis thesis addresses the problem of localization in urban areas. Inferring accurate positioning in the city is important in many applications such as augmented reality or mobile robotics. However, systems based on inertial sensors (IMUs) are subject to significant drifts and GPS data can suffer from a valley effect that limits their accuracy. A natural solution is to rely on the camera pose estimation in computer vision. We notice that buildings are the main visual landmarks of human beings but also objects of interest for augmented reality applications. We therefore aim to compute the camera pose relatively to a database of known reference buildings from a single image. The problem is twofold : find the visible references in the current image (place recognition) and compute the camera pose relatively to them. Conventional approaches to these two sub-problems are challenged in urban environments due to strong perspective effects, frequent repetitions and visual similarity between facades. While specific approaches to these environments have been developed that exploit the high structural regularity of such environments, they still suffer from a number of limitations in terms of detection and recognition of facades as well as pose computation through model registration. The original method developed in this thesis is part of these specific approaches and aims to overcome these limitations in terms of effectiveness and robustness to clutter and changes of viewpoints and illumination. For do so, the main idea is to take advantage of recent advances in deep learning by convolutional neural networks to extract high-level information on which geometric models can be based. Our approach is thus mixed Bottom- Up/Top-Down and is divided into three key stages. We first propose a method to estimate the rotation of the camera pose. The 3 main vanishing points of the image of urban environnement, known as Manhattan vanishing points, are detected by a convolutional neural network (CNN) that estimates both these vanishing points and the image segmentation relative to them. A second refinement step uses this information and image segmentation in a Bayesian model to estimate these points effectively and more accurately. By estimating the camera’s rotation, the images can be rectified and thus free from perspective effects to find the translation. In a second contribution, we aim to detect the facades in these rectified images to recognize them among a database of known buildings and estimate a rough translation. For the sake of efficiency, a series of cues based on facade specific characteristics (repetitions, symmetry, semantics) have been proposed to enable the fast selection of facade proposals. Then they are classified as facade or non-facade according to a new contextual CNN descriptor. Finally, the matching of the detected facades to the references is done by a nearest neighbor search using a metric learned on these descriptors. Eventually we propose a method to refine the estimation of the translation relying on the semantic segmentation inferred by a CNN for its robustness to changes of illumination ans small deformations. If we can already estimate a rough translation from these detected facades, we choose to refine this result by relying on the se- mantic segmentation of the image inferred from a CNN for its robustness to changes of illuminations and small deformations. Since the facade is identified in the previous step, we adopt a model-based approach by registration. Since the problems of registration and segmentation are linked, a Bayesian model is proposed which enables both problems to be jointly solved. This joint processing improves the results of registration and segmentation while remaining efficient in terms of computation time. These three parts have been validated on consistent community data sets. The results show that our approach is fast and more robust to changes in shooting conditions than previous methods
Livros sobre o assunto "Réseaux de neurones bayésiens"
Michel, Verleysen, ed. Les réseaux de neurones artificiels. Paris: Presses universitaires de France, 1996.
Encontre o texto completo da fonteKamp, Yves. Réseaux de neurones récursifs pour mémoires associatives. Lausanne: Presses polytechniques et universitaires romandes, 1990.
Encontre o texto completo da fonteRollet, Guy. Les RÉSEAUX DE NEURONES DE LA CONSCIENCE - Approche multidisciplinaire du phénomène. Paris: Editions L'Harmattan, 2013.
Encontre o texto completo da fontePersonnaz, L. Réseaux de neurones formels pour la modélisation, la commande et la classification. Paris: CNRS Editions, 2003.
Encontre o texto completo da fonteAmat, Jean-Louis. Techniques avancées pour le traitement de l'information: Réseaux de neurones, logique floue, algorithmes génétiques. 2a ed. Toulouse: Cépaduès-Ed., 2002.
Encontre o texto completo da fonteJournées d'électronique (1989 Lausanne, Switzerland). Réseaux de neurones artificiels: Comptes rendus des Journées d'électronique 1989, Lausanne, 10-12 october 1983. Lausanne: Presses polytechniques romande, 1989.
Encontre o texto completo da fontePattern recognition and neural networks. Cambridge: Cambridge University Press, 1996.
Encontre o texto completo da fonteSeidou, Ousmane. Modélisation de la croissance de glace de lac par réseaux de neurones artificiels et estimation du volume de la glace abandonnée sur les berges des réservoirs hydroélectriques pendant les opérations d'hiver. Québec, QC: INRS--ETE, 2005.
Encontre o texto completo da fonteSuzanne, Tyc-Dumont, ed. Le neurone computationnel: Histoire d'un siècle de recherches. Paris: CNRS, 2005.
Encontre o texto completo da fonteBiophysics of computation: Information processing in single neurons. New York: Oxford University Press, 1999.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Réseaux de neurones bayésiens"
Martaj, Dr Nadia, e Dr Mohand Mokhtari. "Réseaux de neurones". In MATLAB R2009, SIMULINK et STATEFLOW pour Ingénieurs, Chercheurs et Etudiants, 807–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11764-0_17.
Texto completo da fonteKipnis, C., e E. Saada. "Un lien entre réseaux de neurones et systèmes de particules: Un modele de rétinotopie". In Lecture Notes in Mathematics, 55–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0094641.
Texto completo da fonteBENMAMMAR, Badr, e Asma AMRAOUI. "Application de l’intelligence artificielle dans les réseaux de radio cognitive". In Gestion et contrôle intelligents des réseaux, 233–60. ISTE Group, 2020. http://dx.doi.org/10.51926/iste.9008.ch9.
Texto completo da fonte"3 Réseaux bayésiens hybrides". In Réseaux bayésiens avec R, 67–86. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1742-9-005.
Texto completo da fonte"3 Réseaux bayésiens hybrides". In Réseaux bayésiens avec R, 67–86. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1742-9.c005.
Texto completo da fonte"Index". In Réseaux bayésiens avec R, 237–40. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1742-9-011.
Texto completo da fonte"Avant-propos". In Réseaux bayésiens avec R, xiii—xvi. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1742-9-002.
Texto completo da fonte"5 Logiciels pour réseaux bayésiens". In Réseaux bayésiens avec R, 131–44. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1742-9-007.
Texto completo da fonte"6 Réseaux bayésiens en grandeur réelle". In Réseaux bayésiens avec R, 145–80. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1742-9-008.
Texto completo da fonte"1 Cas discret : les réseaux bayésiens multinomiaux". In Réseaux bayésiens avec R, 1–36. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1742-9-003.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Réseaux de neurones bayésiens"
Fourcade, A. "Apprentissage profond : un troisième oeil pour les praticiens". In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206601014.
Texto completo da fonteBouejla, A., F. Guarnieri e A. Napoli. "Apport des Réseaux Bayésiens « dynamiques » à la lutte contre la piraterie maritime". In Congrès Lambda Mu 19 de Maîtrise des Risques et Sûreté de Fonctionnement, Dijon, 21-23 Octobre 2014. IMdR, 2015. http://dx.doi.org/10.4267/2042/56118.
Texto completo da fonteGresse, Adrien, Richard Dufour, Vincent Labatut, Mickael Rouvier e Jean-François Bonastre. "Mesure de similarité fondée sur des réseaux de neurones siamois pour le doublage de voix". In XXXIIe Journées d’Études sur la Parole. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/jep.2018-2.
Texto completo da fonteORLIANGES, Jean-Christophe, Younes El Moustakime, Aurelian Crunteanu STANESCU, Ricardo Carrizales Juarez e Oihan Allegret. "Retour vers le perceptron - fabrication d’un neurone synthétique à base de composants électroniques analogiques simples". In Les journées de l'interdisciplinarité 2023. Limoges: Université de Limoges, 2024. http://dx.doi.org/10.25965/lji.761.
Texto completo da fonteWalid, Tazarki, Fareh Riadh e Chichti Jameleddine. "La Prevision Des Crises Bancaires: Un essai de modélisation par la méthode des réseaux de neurones [Not available in English]". In International Conference on Information and Communication Technologies from Theory to Applications - ICTTA'08. IEEE, 2008. http://dx.doi.org/10.1109/ictta.2008.4529985.
Texto completo da fonteKim, Lila, e Cédric Gendrot. "Classification automatique de voyelles nasales pour une caractérisation de la qualité de voix des locuteurs par des réseaux de neurones convolutifs". In XXXIVe Journées d'Études sur la Parole -- JEP 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/jep.2022-82.
Texto completo da fonteGendrot, Cedric, Emmanuel Ferragne e Anaïs Chanclu. "Analyse phonétique de la variation inter-locuteurs au moyen de réseaux de neurones convolutifs : voyelles seules et séquences courtes de parole". In XXXIVe Journées d'Études sur la Parole -- JEP 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/jep.2022-94.
Texto completo da fonteQuintas, Sebastião, Alberto Abad, Julie Mauclair, Virginie Woisard e Julien Pinquier. "Utilisation de réseaux de neurones profonds avec attention pour la prédiction de l’intelligibilité de la parole de patients atteints de cancers ORL". In XXXIVe Journées d'Études sur la Parole -- JEP 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/jep.2022-7.
Texto completo da fonte