Dissertations / Theses on the topic 'Variables hiérarchiques'
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Szafranski, Marie. "Pénalités hiérarchiques pour l'intégration de connaissances dans les modèles statistiques." Compiègne, 2008. http://www.theses.fr/2008COMP1770.
Supervised learning aims at predicting, but also analyzing or interpreting an observed phenomenon. Hierarchical penalization is a generic framework for integrating prior information in the fitting of statistical models. This prior information represents the relations shared by the characteristics of a given studied problem. In this thesis, the characteristics are organized in a two-levels tree structure, which defines distinct groups. The assumption is that few (groups of) characteristics are involved to discriminate between observations. Thus, for a learning problem, the goal is to identify relevant groups of characteristics, and at the same time, the significant characteristics within these groups. An adaptive penalization formulation is used to extract the significant components of each level. We show that the solution to this problem is equivalent to minimize a problem regularized by a mixed norm. These two approaches have been used to study the convexity and sparseness properties of the method. The latter is derived in parametric and non parametric function spaces. Experiences on brain-computer interfaces problems support our approach
Doan, Nath-Quang. "Modèles hiérarchiques et topologiques pour le clustering et la visualisation des données." Paris 13, 2013. http://scbd-sto.univ-paris13.fr/secure/edgalilee_th_2013_doan.pdf.
This thesis focuses on clustering approaches inspired from topological models and an autonomous hierarchical clustering method. The clustering problem becomes more complicated and difficult due to the growth in quality and quantify of structured data such as graphs, trees or sequences. In this thesis, we are particularly interested in self-organizing maps which have been generally used for learning topological preservation, clustering, vector quantization and graph visualization. Our studyconcerns also a hierarchical clustering method AntTree which models the ability of real ants to build structure by connect themselves. By combining the topological map with the self-assembly rules inspired from AntTree, the goal is to represent data in a hierarchical and topological structure providing more insight data information. The advantage is to visualize the clustering results as multiple hierarchical trees and a topological network. In this report, we present three new models that are able to address clustering, visualization and feature selection problems. In the first model, our study shows the interest in the use of hierarchical and topological structure through several applications on numerical datasets, as well as structured datasets e. G. Graphs and biological dataset. The second model consists of a flexible and growing structure which does not impose the strict network-topology preservation rules. Using statistical characteristics provided by hierarchical trees, it accelerates significantly the learning process. The third model addresses particularly the issue of unsupervised feature selection. The idea is to use hierarchical structure provided by AntTree to discover automatically local data structure and local neighbors. By using the tree topology, we propose a new score for feature selection by constraining the Laplacian score. Finally, this thesis offers several perspectives for future work
Szafranski, Marie. "Pénalités hiérarchiques pour l'ntégration de connaissances dans les modèles statistiques." Phd thesis, Université de Technologie de Compiègne, 2008. http://tel.archives-ouvertes.fr/tel-00369025.
Guin, Ophélie. "Méthodes bayésiennes semi-paramétriques d'extraction et de sélection de variables dans le cadre de la dendroclimatologie." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00636704.
Saves, Paul. "High dimensional multidisciplinary design optimization for eco-design aircraft." Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0002.
Nowadays, there has been significant and growing interest in improving the efficiency of vehicle design processes through the development of tools and techniques in the field of multidisciplinary design optimization (MDO). In fact, when optimizing both the aerodynamics and structures, one needs to consider the effect of the aerodynamic shape variables and structural sizing variables on the weight which also affects the fuel consumption. MDO arises as a powerful tool that can perform this trade-off automatically. The objective of the Ph. D project is to propose an efficient approach for solving an aero-structural wing optimization process at the conceptual design level. The latter is formulated as a constrained optimization problem that involves a large number of design variables (typically 700 variables). The targeted optimization approach is based on a sequential enrichment (typically efficient global optimization (EGO)), using an adaptive surrogate model. Kriging surrogate models are one of the most widely used in engineering problems to substitute time-consuming high fidelity models. EGO is a heuristic method, designed for the solution of global optimization problems that has performed well in terms of quality of the solution computed. However, like any other method for global optimization, EGO suffers from the curse of dimensionality, meaning that its performance is satisfactory on lower dimensional problems, but deteriorates as the dimensionality of the optimization search space increases. For realistic aircraft wing design problems, the typical size of the design variables exceeds 700 and, thus, trying to solve directly the problems using EGO is ruled out. In practical test cases, high dimensional MDO problems may possess a lower intrinsic dimensionality, which can be exploited for optimization. In this context, a feature mapping can then be used to map the original high dimensional design variable onto a sufficiently small design space. Most of the existing approaches in the literature use random linear mapping to reduce the dimension, sometimes active learning is used to build this linear embedding. Generalizations to non-linear subspaces are also proposed using the so-called variational autoencoder. For instance, a composition of Gaussian processes (GP), referred as deep GP, can be very useful. In this PhD thesis, we will investigate efficient parameterization tools to significantly reduce the number of design variables by using active learning technics. An extension of the method could be also proposed to handle mixed continuous and categorical inputs using some previous works on low dimensional problems. Practical implementations within the OpenMDAO framework (an open source MDO framework developed by NASA) are expected
El, Maliki Abderrahman. "Résolution de problèmes aux limites à l'aide de méthodes itératives hiérarchiques à préconditionneur variable." Thesis, Université Laval, 2007. http://www.theses.ulaval.ca/2007/24692/24692.pdf.
Lionnet, Cécile. "Une Approche hiérarchique pour l'étude de la variabilité vibro-acoustique basses fréquences dans un habitacle automobile." Compiègne, 2006. http://www.theses.fr/2006COMP1654.
This work deals with an experimental and numerical hierarchical study of the variability of low frequency interior noise levels in passenger cars. It is focused on booming noise. A terminology is introduced to clearly distinguish between intra variability and inter variability respectively corresponding to a lack of robustness toward environmental conditions and manufacturing process. The principle of the hierarchical approach is to analyse the relationship between noise variability (level 1) and the variability of the systems (level 2), here the engine, the engine mounting system and the body. The experimental results show that, for the type of vehicle under study, the intra variability level has the same order of magnitude as the inter variability one: intra variability is of great importance toward the structural acoustic robustness of vehicles. The engine excitations variability levels are negligible, whereas the behaviour of both the engine mounting system and the body respectively show a moderate and high level of variability. The probabilistic numerical study has been carried out using Monte Carlo simulations applied to a noise synthesis model. The propagation of variability between level 1 and level 2 has been underlined on simplified examples. A first attempt of validation of booming noise inter variability is illustrated on an industrial case. An approximate analytical sensitivity analysis is shown and compared to finite difference approximations combined with Monte Carlo simulations
Chastaing, Gaëlle. "Indices de Sobol généralisés par variables dépendantes." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM046.
A mathematical model aims at characterizing a complex system or process that is too expensive to experiment. However, in this model, often strongly non linear, input parameters can be affected by a large uncertainty including errors of measurement of lack of information. Global sensitivity analysis is a stochastic approach whose objective is to identify and to rank the input variables that drive the uncertainty of the model output. Through this analysis, it is then possible to reduce the model dimension and the variation in the output of the model. To reach this objective, the Sobol indices are commonly used. Based on the functional ANOVA decomposition of the output, also called Hoeffding decomposition, they stand on the assumption that the incomes are independent. Our contribution is on the extension of Sobol indices for models with non independent inputs. In one hand, we propose a generalized functional decomposition, where its components is subject to specific orthogonal constraints. This decomposition leads to the definition of generalized sensitivity indices able to quantify the dependent inputs' contribution to the model variability. On the other hand, we propose two numerical methods to estimate these constructed indices. The first one is well-fitted to models with independent pairs of dependent input variables. The method is performed by solving linear system involving suitable projection operators. The second method can be applied to more general models. It relies on the recursive construction of functional systems satisfying the orthogonality properties of summands of the generalized decomposition. In parallel, we illustrate the two methods on numerical examples to test the efficiency of the techniques
Daviet, Hélène. "Class-Add, une procédure de sélection de variables basée sur une troncature k-additive de l'information mutuelle et sur une classification ascendante hiérarchique en pré-traitement." Phd thesis, Université de Nantes, 2009. http://tel.archives-ouvertes.fr/tel-00481931.
Loingeville, Florence. "Modèle linéaire généralisé hiérarchique Gamma-Poisson pour le contrôle de qualité en microbiologie." Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10005/document.
In this thesis, we propose an analysis of variance method for discrete data from quality control in microbiology. To identify the issues of this work, we start by studying the analysis of variance method currently used in microbiology, its benefits, drawbacks, and limits. We propose a first model to respond the problem, corresponding to a linear model with two nested fixed factors. We use the analyse of deviance method to develop significance tests, that proved to be efficient on data sets of proficiency testings in microbiology. We then introduce a new model involving random factors. The randomness of the factors allow to assess and to caracterize the overdispersion observed in results of counts from proficiency testings in microbiology, that is one of the main objectives of this work. The new model corresponds to a Gamma-Poisson Hierarchical Generalized Linear Model with three random factors. We propose a method based on this model to estimate dispersion parameters, fixed, and random effects. We show practical applications of this method to data sets of proficiency testings in microbiology, that prove the goodness of fit of the model to real data. We also develop significance tests of the random factors from this new model, and a new method to assess the performance of the laboratories taking part in a proficiency testing. We finally introduce a near-exact distribution for the product of independent generalized Gamma random variables, in order to characterize the intensity of the Poisson distribution of the model. This approximation, developped from a factorization of the characteristic function, is very precise and can be used to detect outliers
Ancelet, Sophie. "Exploiter l'approche hiérarchique bayésienne pour la modélisation statistique de structures spatiales: application en écologie des populations." Phd thesis, AgroParisTech, 2008. http://pastel.archives-ouvertes.fr/pastel-00004396.
Daviet, Desmier Hélène. "ClassAdd, une procédure de sélection de variables basée sur une troncature k-additive de l'informatique mutuelle et sur une classification ascendante hiérarchique en pré-traitement." Nantes, 2009. http://www.theses.fr/2009NANT2019.
Subset variable selection algorithms are necessary when the number of features is too huge to provide a good understanding of the underlying process that generated the data. In the past few years, variable and feature selection have become the focus of much research because of domains, such as molecular chemistry or gene expression array analysis, with hundreds to tens of thousands of variables. In the framework of subset variable selection for supervised classification involving only discret variables, we propose a selection algorithm using a computationally efficient relevance measure based on a k-additive truncation of the mutual information and involving an agglomerative hierarchical clustering of the set of potentially discriminatory variables in order to reduce the number of subsets whose relevance is estimated
Chagneau, Pierrette. "Modélisation bayésienne hiérarchique pour la prédiction multivariée de processus spatiaux non gaussiens et processus ponctuels hétérogènes d'intensité liée à une variable prédite : application à la prédiction de la régénération en forêt tropicale humide." Montpellier 2, 2009. http://www.theses.fr/2009MON20157.
One of the weak points of forest dynamics models is the recruitment. Classically, ecologists make the assumption that recruitment mainly depends on both spatial pattern of mature trees and environment. A detailed inventory of the stand and the environmental conditions enabled them to show the effects of these two factors on the local density of seedlings. In practice, such information is not available: only a part of seedlings is sampled and the environment is partially observed. The aim of the paper is to propose an approach in order to predict the spatial distribution and the seedlings genotype on the basis of a reasonable sampling of seedling, mature trees and environmental conditions. The spatial pattern of the seedlings is assumed to be a realization of a marked point process. The intensity of the process is not only related to the seed and pollen dispersal but also to the sapling survival. The sapling survival depends on the environment; so the environment must be predicted on the whole study area. The environment is characterized through spatial variables of different nature and predictions are obtained using a spatial hierarchical model. Unlike the existing models which assume the environmental covariables as exactly known, the recruitment model we propose takes into account the error related to the prediction of the environment. The prediction of seedling recruitment in tropical rainforest in French Guiana illustrates our approach
Gondard-Delcroix, Claire. "LA COMBINAISON DES ANALYSES QUALITATIVE ET QUANTITATIVE POUR UNE ETUDE DES DYNAMIQUES DE PAUVRETE EN MILIEU RURAL MALGACHE." Phd thesis, Université Montesquieu - Bordeaux IV, 2006. http://tel.archives-ouvertes.fr/tel-00165502.
Moussouni, Fouzia. "Méthodologie et algorithmes adaptés à l’optimisation multi-niveaux et multi-objectif de systèmes complexes." Thesis, Ecole centrale de Lille, 2009. http://www.theses.fr/2009ECLI0016/document.
The design of an electrical system is a very complex task which needs experts from various fields of competence. In a competitive environment, where technological advance is a key factor, industry seeks to reduce study time and to make solutions reliable by way of a rigorous methodology providing a systemic solution.Then, it is necessary to build models and to develop optimization methods which are suitable with these concerns. Indeed, the optimization of sub-systems without taking into account the interaction does not allow to achieve an optimal system. More complex the system is more the work is difficult and the development time is important because it is difficult for the designer to understand and deal with the system in its complexity. Therefore, it is necessary to integrate the design components in a systemic and holistic approach to take into account, in the same time, the characteristics of a component and its relationship with the system it belongs to.Analytical Target Cascading is a multi-level optimization method for handling complex systems. This hierarchical approach consists on the breaking-down of a complex system into sub-systems, and component where their optimal design is ensured by way of classical optimization algorithms. The optimal solution of the system must be composed of the component's solutions. Then a coordination strategy is needed to ensure consistency of all sub-systems. First, the studied and proposed optimization algorithms are tested and compared on the optimization of electrical components. The second part focuses on the multi-level optimization of complex systems. The optimization of railway traction system is taken as a test case
Lemyre, Gabriel. "Modèles de Markov à variables latentes : matrice de transition non-homogène et reformulation hiérarchique." Thesis, 2021. http://hdl.handle.net/1866/25476.
This master’s thesis is centered on the Hidden Markov Models, a family of models in which an unobserved Markov chain dictactes the behaviour of an observable stochastic process through which a noisy version of the latent chain is observed. These bivariate stochastic processes that can be seen as a natural generalization of mixture models have shown their ability to capture the varying dynamics of many time series and, more specifically in finance, to reproduce the stylized facts of financial returns. In particular, we are interested in discrete-time Markov chains with finite state spaces, with the objective of studying the contribution of their hierarchical formulations and the relaxation of the homogeneity hypothesis for the transition matrix to the quality of the fit and predictions, as well as the capacity to reproduce the stylized facts. We therefore present two hierarchical structures, the first allowing for new interpretations of the relationships between states of the chain, and the second allowing for a more parsimonious parameterization of the transition matrix. We also present three non-homogeneous models, two of which have transition probabilities dependent on observed explanatory variables, and the third in which the probabilities depend on another latent variable. We first analyze the goodness of fit and the predictive power of our models on the series of log returns of the S&P 500 and the exchange rate between canadian and american currencies (CADUSD). We also illustrate their capacity to reproduce the stylized facts, and present interpretations of the estimated parameters for the hierarchical and non-homogeneous models. In general, our results seem to confirm the contribution of hierarchical and non-homogeneous models to these measures of performance. In particular, these results seem to suggest that the incorporation of non-homogeneous dynamics to a hierarchical structure may allow for a more faithful reproduction of the stylized facts—even the slow decay of the autocorrelation functions of squared and absolute returns—and better predictive power, while still allowing for the interpretation of the estimated parameters.
Lamarche, Benoît. "L'effet des régimes de primes de rendement sur la satisfaction à l'égard de la rémunération et le soutien perçu du supérieur immédiat : une question de justice organisationnelle." Mémoire, 2012. http://www.archipel.uqam.ca/5326/1/M12680.pdf.