Academic literature on the topic 'Classification à base de modèles génératifs'
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Journal articles on the topic "Classification à base de modèles génératifs":
Denoyer, Ludovic, and Patrick Gallinari. "Un modèle de mixture de modèles génératifs pour les documents structurés multimédias. Application à la classification de documents XML et HTML." Document numérique 8, no. 3 (September 1, 2004): 35–54. http://dx.doi.org/10.3166/dn.8.3.35-54.
Jeannerat, Hugues. "Des dynamiques territoriales d’innovation aux dynamiques territoriales de valuation." Revue d’Économie Régionale & Urbaine Pub. anticipées (February 22, 2025): 5p—26. http://dx.doi.org/10.3917/reru.pr1.0024.
Fortin, V., T. B. M. J. Ouarda, P. F. Rasmussen, and B. Bobée. "Revue bibliographique des méthodes de prévision des débits." Revue des sciences de l'eau 10, no. 4 (April 12, 2005): 461–87. http://dx.doi.org/10.7202/705289ar.
Bruno, Olivier, Raphaël Chiappini, and Bertrand Groslambert. "Quelle valeur ajoutée pour les banques françaises ?" Revue économique Pub. anticipées, no. 7 (January 31, 2030): 117–41. http://dx.doi.org/10.3917/reco.pr2.0140.
Nabeneza, Serge, Vincent Porphyre, and Fabrice Davrieux. "Caractérisation des miels de l’océan Indien par spectrométrie proche infrarouge : étude de faisabilité." Revue d’élevage et de médecine vétérinaire des pays tropicaux 67, no. 3 (June 27, 2015): 130. http://dx.doi.org/10.19182/remvt.10181.
VODA, Rock, and Jude EGGOH. "Courbe environnementale de Kuznets : un réexamen des canaux de transmission dans les pays en développement." Revue d’Economie Théorique et Appliquée 13, no. 1 (June 30, 2023): 21–40. http://dx.doi.org/10.62519/reta.v13n1a2.
Perusset, Alain. "Éléments de sémiotique catégorielleThéorie, méthode, schémas et pratique." 126, no. 126 (February 3, 2022). http://dx.doi.org/10.25965/as.7443.
Fougeyrollas, Patrick. "Handicap." Anthropen, 2016. http://dx.doi.org/10.17184/eac.anthropen.013.
Francisco, Debora Bernardes, Karine Dal Paz, and Thiago Vinicius Nadaleto Didone. "Patient Factors Associated with Pharmaceutical Interventions for Inpatients at a Brazilian Teaching Hospital." Canadian Journal of Hospital Pharmacy 74, no. 3 (July 5, 2021). http://dx.doi.org/10.4212/cjhp.v74i3.3148.
Kilani, Mondher. "Culture." Anthropen, 2019. http://dx.doi.org/10.17184/eac.anthropen.121.
Dissertations / Theses on the topic "Classification à base de modèles génératifs":
Jacques, Julien. "Contribution à l'apprentissage statistique à base de modèles génératifs pour données complexes." Habilitation à diriger des recherches, Université des Sciences et Technologie de Lille - Lille I, 2012. http://tel.archives-ouvertes.fr/tel-00761184.
Haghebaert, Marie. "Outils et méthodes pour la modélisation de la dynamique des écosystèmes microbiens complexes à partir d'observations expérimentales temporelles : application à la dynamique du microbiote intestinal." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASM036.
This thesis stems from the European project Homo.symbiosus, which investigates the equilibrium transitions of interactions between the host and its intestinal microbiota. To study these transitions, we pursue two directions: the mechanistic modeling of host-microbiota interactions and the analysis of temporal microbial count data.We enriched and simulated a deterministic model of the intestinal crypt using the EDK numerical scheme, particularly studying the impact of different parameters using the Morris Elementary Effects method. This model proved capable of simulating, on one hand, symbiotic and dysbiotic interaction states and, on the other hand, transition scenarios between states of dysbiosis and symbiosis.In parallel, a compartmental ODE model of the colon, inspired by existing studies, was developed and coupled with the crypt model. The thesis contributed to the enhancement of bacterial metabolism modeling and the modeling of innate immunity at the scale of the intestinal mucosa. A numerical exploration allowed us to assess the influence of diet on the steady state of the model and to study the effect of a pathological scenario by mimicking a breach in the epithelial barrier.Furthermore, we developed an approach to analyze microbial data aimed at assessing the deviation of microbial ecosystems undergoing significant environmental disturbances compared to a reference state. This method, based on DMM classification, enables the study of ecosystem equilibrium transitions in cases with few individuals and few time points. Moreover, a curve classification method using the SBM model was applied to investigate the effects of various disturbances on the microbial ecosystem; the results from this study were used to enrich the host-microbiota interaction model
Baelde, Maxime. "Modèles génératifs pour la classification et la séparation de sources sonores en temps-réel." Thesis, Lille 1, 2019. http://www.theses.fr/2019LIL1I058/document.
This thesis is part of the A-Volute company, an audio enhancement softwares editor. It offers a radar that translates multi-channel audio information into visual information in real-time. This radar, although relevant, lacks intelligence because it only analyses the audio stream in terms of energy and not in terms of separate sound sources. The purpose of this thesis is to develop algorithms for classifying and separating sound sources in real time. On the one hand, audio source classification aims to assign a label (e.g. voice) to a monophonic (one label) or polyphonic (several labels) sound. The developed method uses a specific feature, the normalized power spectrum, which is useful in both monophonic and polyphonic cases due to its additive properties of the sound sources. This method uses a generative model that allows to derive a decision rule based on a non-parametric estimation. The real-time constraint is achieved by pre-processing the prototypes with a hierarchical clustering. The results are encouraging on different databases (owned and benchmark), both in terms of accuracy and computation time, especially in the polyphonic case. On the other hand, source separation consists in estimating the sources in terms of signal in a mixture. Two approaches to this purpose were considered in this thesis. The first considers the signals to be found as missing data and estimates them through a generative process and probabilistic modelling. The other approach consists, from sound examples present in a database, in computing optimal transformations of several examples whose combination tends towards the observed mixture. The two proposals are complementary, each having advantages and drawbacks (computation time for the first, interpretability of the result for the second). The experimental results seem promising and allow us to consider interesting research perspectives for each of the proposals
Bouchard, Guillaume. "Les modèles génératifs en classification supervisée et applications à la catégorisation d'images et à la fiabilité industrielle." Phd thesis, Université Joseph Fourier (Grenoble), 2005. http://tel.archives-ouvertes.fr/tel-00541059.
Azeraf, Elie. "Classification avec des modèles probabilistes génératifs et des réseaux de neurones. Applications au traitement des langues naturelles." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. https://theses.hal.science/tel-03880848.
Many probabilistic models have been neglected for classification tasks with supervised learning for several years, as the Naive Bayes or the Hidden Markov Chain. These models, called generative, are criticized because the induced classifier must learn the observations' law. This problem is too complex when the number of observations' features is too large. It is especially the case with Natural Language Processing tasks, as the recent embedding algorithms convert words in large numerical vectors to achieve better scores.This thesis shows that every generative model can define its induced classifier without using the observations' law. This proposition questions the usual categorization of the probabilistic models and classifiers and allows many new applications. Therefore, Hidden Markov Chain can be efficiently applied to Chunking and Naive Bayes to sentiment analysis.We go further, as this proposition allows to define the classifier induced from a generative model with neural network functions. We "neuralize" the models mentioned above and many of their extensions. Models so obtained allow to achieve relevant scores for many Natural Language Processing tasks while being interpretable, able to require little training data, and easy to serve
Vandewalle, Vincent. "Estimation et sélection en classification semi-supervisée." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2009. http://tel.archives-ouvertes.fr/tel-00447141.
Rogouschi, Nicoleta. "Classification à base de modèles de mélanges topologiques des données catégorielles et continues." Paris 13, 2009. http://www.theses.fr/2009PA132015.
The research presented in this thesis concerns the development of self-organising map approaches based on mixture models which deal with different kinds of data : qualitative, mixed and sequential. For each type of data we propose an adapted unsupervised learning model. The first model, described in this work, is a new learning algorithm of topological map BeSOM (Bernoulli Self-Organizing Map) dedicated to binary data. Each map cell is associated with a Bernoulli distribution. In this model, the learning has the objective to estimate the density function presented as a mixture of densities. Each density is as well a mixture of Bernoulli distribution defined on a neighbourhood. The second model touches upon the problem of probability approaches for the mixeddata clustering (quantitative and qualitative). The model is inspired by previous workswhich define a distribution by a mixture of Bernoulli and Gaussian distributions. This approach gives a different dimension to topological map : it allows probability map interpretation and others the possibility to take advantage of local distribution associated with continuous and categorical variables. As for the third model presented in this thesis, it is a new Markov mixture model applied to treatment of the data structured in sequences. The approach that we propose is a generalisation of traditional Markov chains. There are two versions : the global approach, where topology is used implicitly, and the local approach where topology is used explicitly. The results obtained upon the validation of all the methods are encouragingand promising, both for classification and modelling
Chali, Samy. "Robustness Analysis of Classifiers Against Out-of-Distribution and Adversarial Inputs." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST012.
Many issues addressed by AI involve the classification of complex input data that needs to be separated into different classes. The functions that transform the complex input values into a simpler, linearly separable space are achieved either through learning (deep convolutional networks) or by projecting into a high-dimensional space to obtain a 'rich' non-linear representation of the inputs, followed by a linear mapping between the high-dimensional space and the output units, as used in Support Vector Machines (Vapnik's work 1966-1995). The thesis aims to create an optimized, generic architecture capable of preprocessing data to prepare them for classification with minimal operations required. Additionally, this architecture aims to enhance the model's autonomy by enabling continuous learning, robustness to corrupted data, and the identification of data that the model cannot process
Besedin, Andrey. "Continual forgetting-free deep learning from high-dimensional data streams." Electronic Thesis or Diss., Paris, CNAM, 2019. http://www.theses.fr/2019CNAM1263.
In this thesis, we propose a new deep-learning-based approach for online classification on streams of high-dimensional data. In recent years, Neural Networks (NN) have become the primary building block of state-of-the-art methods in various machine learning problems. Most of these methods, however, are designed to solve the static learning problem, when all data are available at once at training time. Performing Online Deep Learning is exceptionally challenging.The main difficulty is that NN-based classifiers usually rely on the assumption that the sequence of data batches used during training is stationary, or in other words, that the distribution of data classes is the same for all batches (i.i.d. assumption).When this assumption does not hold Neural Networks tend to forget the concepts that are temporarily not available in thestream. In the literature, this phenomenon is known as catastrophic forgetting. The approaches we propose in this thesis aim to guarantee the i.i.d. nature of each batch that comes from the stream and compensates for the lack of historical data. To do this, we train generative models and pseudo-generative models capable of producing synthetic samples from classes that are absent or misrepresented in the stream and complete the stream’s batches with these samples. We test our approaches in an incremental learning scenario and a specific type of continuous learning. Our approaches perform classification on dynamic data streams with the accuracy close to the results obtained in the static classification configuration where all data are available for the duration of the learning. Besides, we demonstrate the ability of our methods to adapt to invisible data classes and new instances of already known data categories, while avoiding forgetting the previously acquired knowledge
Zacklad, Manuel. "Principes de modélisation qualitative pour l'aide à la décision dans les organisations : méthode d'utilisation du logiciel d'acquisition des connaissances C-KAT." Compiègne, 1993. http://www.theses.fr/1993COMPD633.
Books on the topic "Classification à base de modèles génératifs":
1943-, Haton Jean-Paul, ed. Le Raisonnement en intelligence artificielle: Modèles, techniques et architectures pour les systèmes à base de connaissances. Paris: InterÉditions, 1991.
Book chapters on the topic "Classification à base de modèles génératifs":
AUBERT, Julie, Pierre BARBILLON, Sophie DONNET, and Vincent MIELE. "Modèles à blocs latents pour la détection de structures dans les réseaux écologiques." In Approches statistiques pour les variables cachées en écologie, 131–50. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9047.ch6.
Reports on the topic "Classification à base de modèles génératifs":
Logan, C. E., H. A. J. Russell, A. K. Burt, A. Burt, R. P. M. Mulligan, D. R. Sharpe, and A. F. Bajc. A three-dimensional surficial geology model of southern Ontario. Natural Resources Canada/CMSS/Information Management, 2024. http://dx.doi.org/10.4095/pudw24j7tx.