Дисертації з теми "Apprentissage automatique – Applications industrielles"
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Langlois, Julien. "Vision industrielle et réseaux de neurones profonds : application au dévracage de pièces plastiques industrielles." Thesis, Nantes, 2019. http://www.theses.fr/2019NANT4010/document.
Повний текст джерелаThis work presents a pose estimation method from a RGB image of industrial parts placed in a bin. In a first time, neural networks are used to segment a certain number of parts in the scene. After applying an object mask to the original image, a second network is inferring the local depth of the part. Both the local pixel coordinates of the part and the local depth are used in two networks estimating the orientation of the object as a quaternion and its translation on the Z axis. Finally, a registration module working on the back-projected local depth and the 3D model of the part is refining the pose inferred from the previous networks. To deal with the lack of annotated real images in an industrial context, an data generation process is proposed. By using various light parameters, the dataset versatility allows to anticipate multiple challenging exploitation scenarios within an industrial environment
Le, Nguyen Minh Huong. "Online machine learning-based predictive maintenance for the railway industry." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT027.
Повний текст джерелаBeing an effective long-distance mass transit, the railway will continue to flourish for its limited carbon footprint in the environment. Ensuring the equipment's reliability and passenger safety brings forth the need for efficient maintenance. Apart from the prevalence of corrective and periodic maintenance, predictive maintenance has come into prominence lately. Recent advances in machine learning and the abundance of data drive practitioners to data-driven predictive maintenance. The common practice is to collect data to train a machine learning model, then deploy the model for production and keep it unchanged afterward. We argue that such practice is suboptimal on a data stream. The unboundedness of the stream makes the model prone to incomplete learning. Dynamic changes on the stream introduce novel concepts unseen by the model and decrease its accuracy. The velocity of the stream makes manual labeling infeasible and disables supervised learning algorithms. Therefore, switching from a static, offline learning paradigm to an adaptive, online one is necessary, especially when new generations of connected trains continuously generating sensor data have already been a reality. We investigate the applicability of online machine learning for predictive maintenance on typical complex systems in the railway. First, we develop InterCE as an active learning-based framework that extracts cycles from an unlabeled stream by interacting with a human expert. Then, we implement a long short-term memory autoencoder to transform the extracted cycles into feature vectors that are more compact yet remain representative. Finally, we design CheMoc as a framework that continuously monitors the condition of the systems using online adaptive clustering. Our methods are evaluated on the passenger access systems on two fleets of passenger trains managed by the national railway company SNCF of France
Teytaud, Olivier. "Apprentissage, réseaux de neurones et applications." Lyon 2, 2001. http://theses.univ-lyon2.fr/documents/lyon2/2001/teytaud_o.
Повний текст джерелаTeytaud, Olivier Paugam-Moisy Hélène. "Apprentissage, réseaux de neurones et applications." [S.l.] : [s.n.], 2001. http://demeter.univ-lyon2.fr:8080/sdx/theses/lyon2/2001/teytaud_o.
Повний текст джерелаZennir, Youcef. "Apprentissage par renforcement et systèmes distribués : application à l'apprentissage de la marche d'un robot hexapode." Lyon, INSA, 2004. http://theses.insa-lyon.fr/publication/2004ISAL0034/these.pdf.
Повний текст джерелаThe goal of this thesis is to study and to develop reinforcement learning techniques in order a hexapod robot to learn to walk. The main assumption on which this work is based is that effective gaits can be obtained as the control of the movements is distributed on each leg rather than centralised in a single decision centre. A distributed approach of the Q-learning technique is adopted in which the agents contributing to the same global objective perform their own learning process taking into account or not the other agents. The centralised and distributed approaches are compared. Different simulations and tests are carried out so as to generate stable periodic gaits. The influence of the learning parameters on the quality of the gaits are studied. The walk appears as an emerging phenomenon from the individual movements of the legs. Problems of fault tolerance and lack of state information are investigated. Finally it is verified that with the developed algorithm the simulated robot learns how to reach a desired trajectory while controlling its posture
Makiou, Abdelhamid. "Sécurité des applications Web : Analyse, modélisation et détection des attaques par apprentissage automatique." Thesis, Paris, ENST, 2016. http://www.theses.fr/2016ENST0084/document.
Повний текст джерелаWeb applications are the backbone of modern information systems. The Internet exposure of these applications continually generates new forms of threats that can jeopardize the security of the entire information system. To counter these threats, there are robust and feature-rich solutions. These solutions are based on well-proven attack detection models, with advantages and limitations for each model. Our work consists in integrating functionalities of several models into a single solution in order to increase the detection capacity. To achieve this objective, we define in a first contribution, a classification of the threats adapted to the context of the Web applications. This classification also serves to solve some problems of scheduling analysis operations during the detection phase of the attacks. In a second contribution, we propose an architecture of Web application firewall based on two analysis models. The first is a behavioral analysis module, and the second uses the signature inspection approach. The main challenge to be addressed with this architecture is to adapt the behavioral analysis model to the context of Web applications. We are responding to this challenge by using a modeling approach of malicious behavior. Thus, it is possible to construct for each attack class its own model of abnormal behavior. To construct these models, we use classifiers based on supervised machine learning. These classifiers use learning datasets to learn the deviant behaviors of each class of attacks. Thus, a second lock in terms of the availability of the learning data has been lifted. Indeed, in a final contribution, we defined and designed a platform for automatic generation of training datasets. The data generated by this platform is standardized and categorized for each class of attacks. The learning data generation model we have developed is able to learn "from its own errors" continuously in order to produce higher quality machine learning datasets
Knyazeva, Elena. "Apprendre par imitation : applications à quelques problèmes d'apprentissage structuré en traitement des langues." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS134/document.
Повний текст джерелаStructured learning has become ubiquitousin Natural Language Processing; a multitude ofapplications, such as personal assistants, machinetranslation and speech recognition, to name just afew, rely on such techniques. The structured learningproblems that must now be solved are becomingincreasingly more complex and require an increasingamount of information at different linguisticlevels (morphological, syntactic, etc.). It is thereforecrucial to find the best trade-off between the degreeof modelling detail and the exactitude of the inferencealgorithm. Imitation learning aims to perform approximatelearning and inference in order to better exploitricher dependency structures. In this thesis, we explorethe use of this specific learning setting, in particularusing the SEARN algorithm, both from a theoreticalperspective and in terms of the practical applicationsto Natural Language Processing tasks, especiallyto complex tasks such as machine translation.Concerning the theoretical aspects, we introduce aunified framework for different imitation learning algorithmfamilies, allowing us to review and simplifythe convergence properties of the algorithms. With regardsto the more practical application of our work, weuse imitation learning first to experiment with free ordersequence labelling and secondly to explore twostepdecoding strategies for machine translation
Mokhtari, Myriam. "Réseau neuronal aléatoire : applications à l'apprentissage et à la reconnaissance d'images." Paris 5, 1994. http://www.theses.fr/1994PA05S019.
Повний текст джерелаBély, Marina. "Détection automatique et correction des carences en azote assimilable des fermentations alcooliques en conditions œnologiques : étude cinétique et approche physiologique." Montpellier 2, 1990. http://www.theses.fr/1990MON20292.
Повний текст джерелаBérard, Alexandre. "Neural machine translation architectures and applications." Thesis, Lille 1, 2018. http://www.theses.fr/2018LIL1I022/document.
Повний текст джерелаThis thesis is centered on two main objectives: adaptation of Neural Machine Translation techniques to new tasks and research replication. Our efforts towards research replication have led to the production of two resources: MultiVec, a framework that facilitates the use of several techniques related to word embeddings (Word2vec, Bivec and Paragraph Vector); and a framework for Neural Machine Translation that implements several architectures and can be used for regular MT, Automatic Post-Editing, and Speech Recognition or Translation. These two resources are publicly available and now extensively used by the research community. We extend our NMT framework to work on three related tasks: Machine Translation (MT), Automatic Speech Translation (AST) and Automatic Post-Editing (APE). For the machine translation task, we replicate pioneer neural-based work, and do a case study on TED talks where we advance the state-of-the-art. Automatic speech translation consists in translating speech from one language to text in another language. In this thesis, we focus on the unexplored problem of end-to-end speech translation, which does not use an intermediate source-language text transcription. We propose the first model for end-to-end AST and apply it on two benchmarks: translation of audiobooks and of basic travel expressions. Our final task is automatic post-editing, which consists in automatically correcting the outputs of an MT system in a black-box scenario, by training on data that was produced by human post-editors. We replicate and extend published results on the WMT 2016 and 2017 tasks, and propose new neural architectures for low-resource automatic post-editing
Burel, Gilles. "RESEAUX DE NEURONES EN TRAITEMENT D'IMAGES - Des Modèles théoriques aux Applications Industrielles -." Phd thesis, Université de Bretagne occidentale - Brest, 1991. http://tel.archives-ouvertes.fr/tel-00101699.
Повний текст джерелаtraitement du signal et de l'image. On se place d'emblée du point de vue de
l'industriel impliqué dans la recherche, c'est à dire que l'on s'intéresse à
des problèmes réalistes, sans pour autant négliger la recherche
théorique.
Dans une première partie, nous montrons
l'intérêt des réseaux de neurones comme source d'inspiration pour la
conception de nouveaux algorithmes. Nous proposons en particulier une
structure originale pour la prédiction, ainsi que de nouveaux algorithmes de
Quantification Vectorielle. Les propriétés des algorithmes existants sont
également éclaircies du point de vue théorique, et des méthodes de réglage
automatique de leurs paramètres sont proposées.
On montre ensuite les capacités des réseaux de neurones à traiter un vaste champ
d'applications d'intérêt industriel. Pour divers problèmes de traitement de
l'image et du signal (de la segmentation à la séparation de sources, en
passant par la reconnaissance de formes et la compression de données), on
montre qu'il est possible de développer à moindre coût une solution neuronale
efficace.
Bertrand, Hadrien. "Optimisation d'hyper-paramètres en apprentissage profond et apprentissage par transfert : applications en imagerie médicale." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT001/document.
Повний текст джерелаIn the last few years, deep learning has changed irrevocably the field of computer vision. Faster, giving better results, and requiring a lower degree of expertise to use than traditional computer vision methods, deep learning has become ubiquitous in every imaging application. This includes medical imaging applications. At the beginning of this thesis, there was still a strong lack of tools and understanding of how to build efficient neural networks for specific tasks. Thus this thesis first focused on the topic of hyper-parameter optimization for deep neural networks, i.e. methods for automatically finding efficient neural networks on specific tasks. The thesis includes a comparison of different methods, a performance improvement of one of these methods, Bayesian optimization, and the proposal of a new method of hyper-parameter optimization by combining two existing methods: Bayesian optimization and Hyperband.From there, we used these methods for medical imaging applications such as the classification of field-of-view in MRI, and the segmentation of the kidney in 3D ultrasound images across two populations of patients. This last task required the development of a new transfer learning method based on the modification of the source network by adding new geometric and intensity transformation layers.Finally this thesis loops back to older computer vision methods, and we propose a new segmentation algorithm combining template deformation and deep learning. We show how to use a neural network to predict global and local transformations without requiring the ground-truth of these transformations. The method is validated on the task of kidney segmentation in 3D US images
Soulier, Bruno. "Sur la modélisation expérimentale en mécanique : précision, optimisation et applications industrielles." Cachan, Ecole normale supérieure, 1994. http://www.theses.fr/1994DENS0020.
Повний текст джерелаKannan, Hariprasad. "Quelques applications de l’optimisation numérique aux problèmes d’inférence et d’apprentissage." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLC067/document.
Повний текст джерелаNumerical optimization and machine learning have had a fruitful relationship, from the perspective of both theory and application. In this thesis, we present an application oriented take on some inference and learning problems. Linear programming relaxations are central to maximum a posteriori (MAP) inference in discrete Markov Random Fields (MRFs). Especially, inference in higher-order MRFs presents challenges in terms of efficiency, scalability and solution quality. In this thesis, we study the benefit of using Newton methods to efficiently optimize the Lagrangian dual of a smooth version of the problem. We investigate their ability to achieve superior convergence behavior and to better handle the ill-conditioned nature of the formulation, as compared to first order methods. We show that it is indeed possible to obtain an efficient trust region Newton method, which uses the true Hessian, for a broad range of MAP inference problems. Given the specific opportunities and challenges in the MAP inference formulation, we present details concerning (i) efficient computation of the Hessian and Hessian-vector products, (ii) a strategy to damp the Newton step that aids efficient and correct optimization, (iii) steps to improve the efficiency of the conjugate gradient method through a truncation rule and a pre-conditioner. We also demonstrate through numerical experiments how a quasi-Newton method could be a good choice for MAP inference in large graphs. MAP inference based on a smooth formulation, could greatly benefit from efficient sum-product computation, which is required for computing the gradient and the Hessian. We show a way to perform sum-product computation for trees with sparse clique potentials. This result could be readily used by other algorithms, also. We show results demonstrating the usefulness of our approach using higher-order MRFs. Then, we discuss potential research topics regarding tightening the LP relaxation and parallel algorithms for MAP inference.Unsupervised learning is an important topic in machine learning and it could potentially help high dimensional problems like inference in graphical models. We show a general framework for unsupervised learning based on optimal transport and sparse regularization. Optimal transport presents interesting challenges from an optimization point of view with its simplex constraints on the rows and columns of the transport plan. We show one way to formulate efficient optimization problems inspired by optimal transport. This could be done by imposing only one set of the simplex constraints and by imposing structure on the transport plan through sparse regularization. We show how unsupervised learning algorithms like exemplar clustering, center based clustering and kernel PCA could fit into this framework based on different forms of regularization. We especially demonstrate a promising approach to address the pre-image problem in kernel PCA. Several methods have been proposed over the years, which generally assume certain types of kernels or have too many hyper-parameters or make restrictive approximations of the underlying geometry. We present a more general method, with only one hyper-parameter to tune and with some interesting geometric properties. From an optimization point of view, we show how to compute the gradient of a smooth version of the Schatten p-norm and how it can be used within a majorization-minimization scheme. Finally, we present results from our various experiments
Nguyen, Bang Giang. "Classification en espaces fonctionnels utilisant la norme BV avec applications aux images ophtalmologiques et à la complexité du trafic aérien." Toulouse 3, 2014. http://thesesups.ups-tlse.fr/2473/.
Повний текст джерелаIn this thesis, we deal with two different problems using Total Variation concept. The first problem concerns the classification of vasculitis in multiple sclerosis fundus angiography, aiming to help ophthalmologists to diagnose such autoimmune diseases. It also aims at determining potential angiography details in intermediate uveitis in order to help diagnosing multiple sclerosis. The second problem aims at developing new airspace congestion metric, which is an important index that is used for improving Air Traffic Management (ATM) capacity. In the first part of this thesis, we provide preliminary knowledge required to solve the above-mentioned problems. First, we present an overview of the Total Variation and express how it is used in our methods. Then, we present a tutorial on Support Vector Machines (SVMs) which is a learning algorithm used for classification and regression. In the second part of this thesis, we first provide a review of methods for segmentation and measurement of blood vessel in retinal image that is an important step in our method. Then, we present our proposed method for classification of retinal images. First, we detect the diseased region in the pathological images based on the computation of BV norm at each point along the centerline of the blood vessels. Then, to classify the images, we introduce a feature extraction strategy to generate a set of feature vectors that represents the input image set for the SVMs. After that, a standard SVM classifier is applied in order to classify the images. Finally, in the third part of this thesis, we address two applications of TV in the ATM domain. In the first application, based on the ideas developed in the second part, we introduce a methodology to extract the main air traffic flows in the airspace. Moreover, we develop a new airspace complexity indicator which can be used to organize air traffic at macroscopic level. This indicator is then compared to the regular density metric which is computed just by counting the number of aircraft in the airspace sector. The second application is based on a dynamical system model of air traffic. We propose a method for developing a new traffic complexity metric by computing the local vectorial total variation norm of the relative deviation vector field. Its aim is to reduce complexity. Three different traffic situations are investigated to evaluate the fitness of the proposed method
Crémilleux, Bruno. "Induction automatique : aspects théoriques, le système ARBRE, applications en médecine." Phd thesis, Grenoble 1, 1991. http://tel.archives-ouvertes.fr/tel-00339492.
Повний текст джерелаBindel, Sébastien. "Algorithmique et applications pour les flottes hétérogènes multiniveaux de matériels mobiles communicants autonomes." Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0172/document.
Повний текст джерелаUnmanned vehicles are defined as autonomous entities with no operator on board. They are a part of a global system called Unmanned System which also includes elements such as a control station. These vehicles are designed to fulfil the requirements of assigned missions and can be deployed in spatial, aerial, terrestrial and maritime environments. Since a mission cannot be accomplished with a single vehicle, vehicles have to cooperate in order to achieve a global mission. However, cooperation requires communication interoperability between all vehicles. Even if previous works have standardized application protocols, it is not sufficient to ensure data delivery between all vehicles, since they have a specific mobility pattern and sometimes different network interfaces. The main goal of this thesis is to offer a seamless network, including all kinds of unmanned systems. We propose a cross layer approach in order to route and deliver data to any vehicle. In this context, each vehicle is able to transmit data to another without information on the global topology. We have developed a routing protocol, which adapts its strategy, according to the contextand to the network environment. In addition, we exploit the any cast diffusion technique based on vehicles features in order to adopt an optimal routing scheme
Kallas, Maya. "Méthodes à noyaux en reconnaissance de formes, prédiction et classification : applications aux biosignaux." Troyes, 2012. http://www.theses.fr/2012TROY0026.
Повний текст джерелаThe proliferation of kernel methods lies essentially on the kernel trick, which induces an implicit nonlinear transformation with reduced computational cost. Still, the inverse transformation is often necessary. The resolution of this so-called pre-image problem enables new fields of applications of these methods. The main purpose of this thesis is to show that recent advances in statistical learning theory provide relevant solutions to several issues raised in signal and image processing. The first part focuses on the pre-image problem, and on solutions with constraints imposed by physiology. The non-negativity is probably the most commonly stated constraints when dealing with natural signals and images. Nonnegativity constraints on the result, as well as on the additivity of the contributions, are studied. The second part focuses on time series analysis according to a predictive approach. Autoregressive models are developed in the transformed space, while the prediction requires solving the pre-image problem. Two kernelbased predictive models are considered: the first one is derived by solving a least-squares problem, and the second one by providing the adequate Yule-Walker equations. The last part deals with the classification task for electrocardiograms, in order to detect anomalies. Detection and multi-class classification are explored in the light of support vector machines and self-organizing maps
Flamary, Rémi. "Apprentissage statistique pour le signal : applications aux interfaces cerveau-machine." Phd thesis, Université de Rouen, 2011. http://tel.archives-ouvertes.fr/tel-00687501.
Повний текст джерелаBréhélin, Laurent. "Modèles de Markov cachés et apprentissage pas fusions d'états : algorithmes, applications, utilisations pour le test de circuits intégrés." Montpellier 2, 2001. http://www.theses.fr/2001MON20051.
Повний текст джерелаFauvel, Kevin. "Enhancing performance and explainability of multivariate time series machine learning methods : applications for social impact in dairy resource monitoring and earthquake early warning." Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S043.
Повний текст джерелаThe prevalent deployment and usage of sensors in a wide range of sectors generate an abundance of multivariate data which has proven to be instrumental for researches, businesses and policies. More specifically, multivariate data which integrates temporal evolution, i.e. Multivariate Time Series (MTS), has received significant interests in recent years, driven by high resolution monitoring applications (e.g. healthcare, mobility) and machine learning. However, for many applications, the adoption of machine learning methods cannot rely solely on their prediction performance. For example, the European Union’s General Data Protection Regulation, which became enforceable on 25 May 2018, introduces a right to explanation for all individuals so that they can obtain “meaningful explanations of the logic involved” when automated decision-making has “legal effects” on individuals or similarly “significantly affecting” them. The current best performing state-of-the-art MTS machine learning methods are “black-box” models, i.e. complicated-to-understand models, which rely on explainability methods providing explanations from any machine learning model to support their predictions (post-hoc model-agnostic). The main line of work in post-hoc model-agnostic explainability methods approximates the decision surface of a model using an explainable surrogate model. However, the explanations from the surrogate models cannot be perfectly faithful with respect to the original model, which is a prerequisite for numerous applications. Faithfulness is critical as it corresponds to the level of trust an end-user can have in the explanations of model predictions, i.e. the level of relatedness of the explanations to what the model actually computes. This thesis introduces new approaches to enhance both performance and explainability of MTS machine learning methods, and derive insights from the new methods about two real-world applications
Jeong, Seong-Gyun. "Modélisation de structures curvilignes et ses applications en vision par ordinateur." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4086/document.
Повний текст джерелаIn this dissertation, we propose curvilinear structure reconstruction models based on stochastic modeling and ranking learning system. We assume that the entire line network can be decomposed into a set of line segments with variable lengths and orientations. This assumption enables us to reconstruct arbitrary shapes of curvilinear structure for different types of datasets. We compute curvilinear feature descriptors based on the image gradient profiles and the morphological profiles. For the stochastic model, we propose prior constraints that define the spatial interaction of line segments. To obtain an optimal configuration corresponding to the latent curvilinear structure, we combine multiple line hypotheses which are computed by MCMC sampling with different parameter sets. Moreover, we learn a ranking function which predicts the correspondence of the given line segment and the latent curvilinear structures. A novel graph-based method is proposed to infer the underlying curvilinear structure using the output rankings of the line segments. We apply our models to analyze curvilinear structure on static images. Experimental results on wide types of datasets demonstrate that the proposed curvilinear structure modeling outperforms the state-of-the-art techniques
García, Durán Alberto. "Learning representations in multi-relational graphs : algorithms and applications." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2271/document.
Повний текст джерелаInternet provides a huge amount of information at hand in such a variety of topics, that now everyone is able to access to any kind of knowledge. Such a big quantity of information could bring a leap forward in many areas if used properly. This way, a crucial challenge of the Artificial Intelligence community has been to gather, organize and make intelligent use of this growing amount of available knowledge. Fortunately, important efforts have been made in gathering and organizing knowledge for some time now, and a lot of structured information can be found in repositories called Knowledge Bases (KBs). A main issue with KBs is that they are far from being complete. This thesis proposes several methods to add new links between the existing entities of the KB based on the learning of representations that optimize some defined energy function. We also propose a novel application to make use of this structured information to generate questions in natural language
Genest, Diane. "Imaging of the fish embryo model and applications to toxicology." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC2008/document.
Повний текст джерелаNumerous chemicals are used as ingredients by the cosmetics industry and are included in cosmetics formula. Aside from the assessment of their efficacy, the cosmetics industry especially needs to assess the safety of their chemicals for human. Toxicological screening of chemicals is performed with the aim of revealing the potential toxic effect of the tested chemical. Among the potential effects we want to detect, the developmental toxicity of the chemical (teratogenicity), meaning its capability of provoking abnormalities during the embryonic development, is crucial. With respect to the international regulations that forbid the use of animal testing for the safety assessment of cosmetics, the toxicological assessment of chemicals must base on an ensemble of in silico assays, in vitro assays and alternative models based assays. For now, a few alternative methods have been validated in the field of developmental toxicology. The development of new alternative methods is thus required. In addition to the safety assessment, the environmental toxicity assessment is also required. The use of most of cosmetics and personal care products leads to their rejection in waterways after washing and rince. This results in the exposition of some aquatic environments (surface waters and coastal marine environments) to chemicals included in cosmetics and personal care products. Thus, the environmental assessment of cosmetics and of their ingredients requires the knowledge of their toxicity on organisms that are representative of aquatic food chains. In this context, the fish embryo model, which is ethically acceptable according to international regulations, presents a dual advantage for the cosmetics industry. Firstly, as a model representative of aquatic organisms, it is accurate for the environmental assessment of chemicals. Secondly, this model is promising for the assessment of the teratogenic effect of chemicals on human. For this reason, a teratogenicity assessment test is developed. This test is based on the analysis of medaka fish embryos (Oryzias Latipes) at 9 days post fertilization, after balneation in a predetermined concentration of the chemical under study. The analysis of functional and morphological parameters allows to calculate a teratogenicity index, that depends on both rates of dead and malformed embryos. This index allows to to draw a conclusion concerning the teratogenic effect of the chemical.The objective of this project is to automate the teratogenicity test, by automated image and video classification. A first method is developed that aims to automatically detect embryo heart beats from acquired video sequences. This method will allow to calculate the proportion of dead embryos. We then focus on the detection of two common malformations: axial malformations and absence of a swim bladder, based on a machine learning classification. This analysis must be completed by the detection of other malformations so that we can measure the rate of malformed embryos and thus, calculate the teratogenicity index of the tested chemical
Belilovsky, Eugene. "Apprentissage de graphes structuré et parcimonieux dans des données de haute dimension avec applications à l’imagerie cérébrale." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLC027.
Повний текст джерелаThis dissertation presents novel structured sparse learning methods on graphs that address commonly found problems in the analysis of neuroimaging data as well as other high dimensional data with few samples. The first part of the thesis proposes convex relaxations of discrete and combinatorial penalties involving sparsity and bounded total variation on a graph as well as bounded `2 norm. These are developed with the aim of learning an interpretable predictive linear model and we demonstrate their effectiveness on neuroimaging data as well as a sparse image recovery problem.The subsequent parts of the thesis considers structure discovery of undirected graphical models from few observational data. In particular we focus on invoking sparsity and other structured assumptions in Gaussian Graphical Models (GGMs). To this end we make two contributions. We show an approach to identify differences in Gaussian Graphical Models (GGMs) known to have similar structure. We derive the distribution of parameter differences under a joint penalty when parameters are known to be sparse in the difference. We then show how this approach can be used to obtain confidence intervals on edge differences in GGMs. We then introduce a novel learning based approach to the problem structure discovery of undirected graphical models from observational data. We demonstrate how neural networks can be used to learn effective estimators for this problem. This is empirically shown to be flexible and efficient alternatives to existing techniques
Zaarour, Iyad. "Contributions à la découverte des stratégies d'écriture d'élèves de scolarité primaire." Rouen, 2004. http://www.theses.fr/2004ROUES001.
Повний текст джерелаThe aim of this study is to bring a contribution to the realization of the evolution follow-up in writing among typical pupils in primary education. For this purpose, we have developed a software for the acquisition of handwritten tracings and the automatic extraction of features from these tracings. Distributed on three periods of about six months each, the acquisitions have therefore been achieved three times for the same pupils in the same experimental conditions, these tracings being acquired online by means of a digitizer. An unsupervised classification is first applied on a set of dynamic features chosen by an expert in the field of child's development psychology; strong forms are thus selected as steady clusters from the obtained partitions. With this unsupervised approach, we have thus discovered three strategies: a first one which is performant in control and global planning, a second one labeled local in control and planning, and a third one which is an unstable intermediary strategy. Next we modeled our problem by means of a probabilistic graphical model (bayesian network) in which the writing strategy is represented by a hidden variable. We build a global hierarchical model in order to link local and global strategies and model the probabilistic dependance between variables and strategies. Our hierarchical model, learnt with real data, enables us to discover two global strategies that correspond to normo-writer pupils and more advanced normo-writers. These two strategies are consistent: the distribution of typical pupils by school level is constant over time, and the probability of transition between (or within) these strategies is also constant over time
Bilodeau, Anthony. "Apprentissage faiblement supervisé appliqué à la segmentation d'images de protéines neuronales." Master's thesis, Université Laval, 2020. http://hdl.handle.net/20.500.11794/39752.
Повний текст джерелаThèse ou mémoire avec insertion d'articles
Tableau d'honneur de la Faculté des études supérieures et postdoctorales, 2020-2021
En biologie cellulaire, la microscopie optique est couramment utilisée pour visualiser et caractériser la présence et la morphologie des structures biologiques. Suite à l’acquisition, un expert devra effectuer l’annotation des structures pour quantification. Cette tâche est ardue, requiert de nombreuses heures de travail, parfois répétitif, qui peut résulter en erreurs d’annotations causées par la fatigue d’étiquetage. L’apprentissage machine promet l’automatisation de tâches complexes à partir d’un grand lot de données exemples annotés. Mon projet de maîtrise propose d’utiliser des techniques faiblement supervisées, où les annotations requises pour l’entraînement sont réduites et/ou moins précises, pour la segmentation de structures neuronales. J’ai d’abord testé l’utilisation de polygones délimitant la structure d’intérêt pour la tâche complexe de segmentation de la protéine neuronale F-actine dans des images de microscopie à super-résolution. La complexité de la tâche est supportée par la morphologie hétérogène des neurones, le nombre élevé d’instances à segmenter dans une image et la présence de nombreux distracteurs. Malgré ces difficultés, l’utilisation d’annotations faibles a permis de quantifier un changement novateur de la conformation de la protéine F-actine en fonction de l’activité neuronale. J’ai simplifié davantage la tâche d’annotation en requérant seulement des étiquettes binaires renseignant sur la présence des structures dans l’image réduisant d’un facteur 30 le temps d’annotation. De cette façon, l’algorithme est entraîné à prédire le contenu d’une image et extrait ensuite les caractéristiques sémantiques importantes pour la reconnaissance de la structure d’intérêt à l’aide de mécanismes d’attention. La précision de segmentation obtenue sur les images de F-actine est supérieure à celle des annotations polygonales et équivalente à celle des annotations précises d’un expert. Cette nouvelle approche devrait faciliter la quantification des changements dynamiques qui se produisent sous le microscope dans des cellules vivantes et réduire les erreurs causées par l’inattention ou le biais de sélection des régions d’intérêt dans les images de microscopie.
In cell biology, optical microscopy is commonly used to visualize and characterize the presenceand morphology of biological structures. Following the acquisition, an expert will have toannotate the structures for quantification. This is a difficult task, requiring many hours ofwork, sometimes repetitive, which can result in annotation errors caused by labelling fatigue.Machine learning promises to automate complex tasks from a large set of annotated sampledata. My master’s project consists of using weakly supervised techniques, where the anno-tations required for training are reduced and/or less precise, for the segmentation of neuralstructures.I first tested the use of polygons delimiting the structure of interest for the complex taskof segmentation of the neuronal protein F-actin in super-resolution microscopy images. Thecomplexity of the task is supported by the heterogeneous morphology of neurons, the highnumber of instances to segment in an image and the presence of many distractors. Despitethese difficulties, the use of weak annotations has made it possible to quantify an innovativechange in the conformation of the F-actin protein as a function of neuronal activity. I furthersimplified the annotation task by requiring only binary labels that indicate the presence ofstructures in the image, reducing annotation time by a factor of 30. In this way, the algorithmis trained to predict the content of an image and then extract the semantic characteristicsimportant for recognizing the structure of interest using attention mechanisms. The segmen-tation accuracy obtained on F-actin images is higher than that of polygonal annotations andequivalent to that of an expert’s precise annotations. This new approach should facilitate thequantification of dynamic changes that occur under the microscope in living cells and reduceerrors caused by inattention or bias in the selection of regions of interest in microscopy images.
Bastos, castro Marcio. "Optimisation de la performance des applications de mémoire transactionnelle sur des plates-formes multicoeurs : une approche basée sur l'apprentissage automatique." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00766983.
Повний текст джерелаCastro, Márcio. "Optimisation de la performance des applications de mémoire transactionnelle sur des plates-formes multicoeurs : une approche basée sur l'apprentissage automatique." Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENM074/document.
Повний текст джерелаMulticore processors are now a mainstream approach to deliver higher performance to parallel applications. In order to develop efficient parallel applications for those platforms, developers must take care of several aspects, ranging from the architectural to the application level. In this context, Transactional Memory (TM) appears as a programmer friendly alternative to traditional lock-based concurrency for those platforms. It allows programmers to write parallel code as transactions, which are guaranteed to execute atomically and in isolation regardless of eventual data races. At runtime, transactions are executed speculatively and conflicts are solved by re-executing conflicting transactions. Although TM intends to simplify concurrent programming, the best performance can only be obtained if the underlying runtime system matches the application and platform characteristics. The contributions of this thesis concern the analysis and improvement of the performance of TM applications based on Software Transactional Memory (STM) on multicore platforms. Firstly, we show that the TM model makes the performance analysis of TM applications a daunting task. To tackle this problem, we propose a generic and portable tracing mechanism that gathers specific TM events, allowing us to better understand the performances obtained. The traced data can be used, for instance, to discover if the TM application presents points of contention or if the contention is spread out over the whole execution. Our tracing mechanism can be used with different TM applications and STM systems without any changes in their original source codes. Secondly, we address the performance improvement of TM applications on multicores. We point out that thread mapping is very important for TM applications and it can considerably improve the global performances achieved. To deal with the large diversity of TM applications, STM systems and multicore platforms, we propose an approach based on Machine Learning to automatically predict suitable thread mapping strategies for TM applications. During a prior learning phase, we profile several TM applications running on different STM systems to construct a predictor. We then use the predictor to perform static or dynamic thread mapping in a state-of-the-art STM system, making it transparent to the users. Finally, we perform an experimental evaluation and we show that the static approach is fairly accurate and can improve the performance of a set of TM applications by up to 18%. Concerning the dynamic approach, we show that it can detect different phase changes during the execution of TM applications composed of diverse workloads, predicting thread mappings adapted for each phase. On those applications, we achieve performance improvements of up to 31% in comparison to the best static strategy
Jiao, Yang. "Applications of artificial intelligence in e-commerce and finance." Thesis, Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0002/document.
Повний текст джерелаArtificial Intelligence has penetrated into every aspect of our lives in this era of Big Data. It has brought revolutionary changes upon various sectors including e-commerce and finance. In this thesis, we present four applications of AI which improve existing goods and services, enables automation and greatly increase the efficiency of many tasks in both domains. Firstly, we improve the product search service offered by most e-commerce sites by using a novel term weighting scheme to better assess term importance within a search query. Then we build a predictive model on daily sales using a time series forecasting approach and leverage the predicted results to rank product search results in order to maximize the revenue of a company. Next, we present the product categorization challenge we hold online and analyze the winning solutions, consisting of the state-of-the-art classification algorithms, on our real dataset. Finally, we combine skills acquired previously from time series based sales prediction and classification to predict one of the most difficult but also the most attractive time series: stock. We perform an extensive study on every single stocks of S&P 500 index using four state-of-the-art classification algorithms and report very promising results
Usunier, Nicolas. "Apprentissage de fonctions d'ordonnancement : une étude théorique de la réduction à la classification et deux applications à la recherche d'information." Paris 6, 2006. http://www.theses.fr/2006PA066425.
Повний текст джерелаRukubayihunga, Koumba Alia. "Vers un système interactif de réalité augmentée mobile pour la supervision de scénarios de maintenance industrielle." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLE051/document.
Повний текст джерелаBy providing to the user relevant data at the right time in the right place, augmented reality might increase productivity and performance of industrial maintenance workers.However, implemented scenario tracking systems often need manual intervention. This may induce insidiously less of concentration, eyestrain, psychological and physical fatigue. Moreover, the operators are the sole judge of the maintenance task execution.It is in this context that Wassa company, specialized in web and mobile solutions, and IRA2 team of IBISC Laboratory, specialized in augmented reality, virtual reality and robotics, have initiated this PhD thesis. The goal is to design an augmented reality system which will :- provide automatically and in real time maintenance instructions to the user .- control, through the mobile devices, the maintenance task achievement and identifythe mistakes done by the operator. A notification will be displayed to report to the userthat the task is incorrectly performed or that a mechanical piece is inaccurately placed.The system should store these errors.- run on any mobile devices by taking into account their computation and storage limitations
Brouard, Thierry. "Algorithmes hybrides d'apprentissage de chaines de Markov cachées : conception et applications à la reconnaissance des formes." Tours, 1999. http://www.theses.fr/1999TOUR4002.
Повний текст джерелаThe main point of this work is based on the quality of modelization of data (called observations) made by hidden Markov models (HMMs). Our goal is to propose algorithms that improve this quality. The criterion used to quantify the quality of HMM is the probability that a given model generates a given observation. To solve this problem, we use a genetic hybridization of HMM. Using genetic algorithms (GAs) jointly to HMM permits two things. First, GAs let us to explore more efficiently the set of models, avoiding local optima. Second, GAs optimize an important characteristic of HMM : its number of hidden states. The most efficient hybrid algorithm finds the best HMM for a given problem, by itself. This means that the GA designs a set of states and the associated transition probabilities. Many explications have been done in the framework of this thesis, in many domains like image recognition, time series prediction, unsupervised image segmentation and object tracking in sequences of images. The new algorithms proposed here are appliable to all domains (peovided that hypothesis related to HMM are satisfied). They allow a fast and efficient training of HMM, and an entirely automatic determination of the architecture (number of states, transition probabilities) of the HMM
Martinelli, Julien. "On learning mechanistic models from time series data with applications to personalised chronotherapies." Thesis, Institut polytechnique de Paris, 2022. https://tel.archives-ouvertes.fr/tel-03686289.
Повний текст джерелаMathematical modeling of biological processes aims at providing formal repre-sentations of complex systems to enable their study, both in a qualitative and quan-titative fashion. The need for explainability suggests the recourse to mechanisticmodels, which explicitly describe molecular interactions. Nevertheless, such mod-els currently rely on the existence of prior knowledge on the underlying reactionnetwork structure. Moreover, their conception remains an art which necessitatescreativity combined to multiple interactions with analysis and data fitting tools.This rules out numerous applications conceivable in personalized medicine, andcalls for methodological advances towards machine learning of patient-tailoredmodels. This thesis intends to devise algorithms to learn models of dynamicalinteractions from temporal data, with an emphasis on explainability for the humanmodeler. Its applications are in the context of personalized chronotherapies, thatconsist in optimizing drug administration with respect to the patient’s biologicalrhythms over the 24-hour span. Three main themes are explored: mechanisticmodeling, network inference and treatment personalization. The first chapter de-scribes the development of the first quantitative mechanistic model of the cellularcircadian clock integrating transcriptomic, proteomic and sub-cellular localizationdata. This model has been successfully connected to a model of cellular pharmacol-ogy of an anticancerous drug, irinotecan, achieving personalization of its optimaladministration timing. The second chapter introduces a novel protocol for inferringwhole-body systemic controls enforced on peripheral clocks. On the long run, thisapproach will make it possible to integrate individual data collected from wearablesfor personalized chronotherapies. The third chapter presents a general algorithmto infer reactions with chemical kinetics from time series data
Martinez, Cristian. "Grammaires locales étendues : principes, mise en œuvre et applications pour l’extraction de l’information." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1075/document.
Повний текст джерелаLocal grammars constitute a descriptive formalism of linguistic phenomena and are commonly represented using directed graphs. Local grammars are used to recognize and extract patterns in a text, but they had some inherent limits in dealing with unexpected variations as well as in their capacity to access exogenous knowledge, in other words information to extract, during the analysis, from external resources and which may be useful to normalize, enhance validate or link the recognized patterns. In this thesis, we introduce the notion of extended local grammar, a formalism capable to extend the classic model of local grammars. The means are twofold: on the one hand, it is achieved by adding arbitrary conditional-functions, called extended functions, which are not predefined in advance and are evaluated from outside of the grammar. On the other hand, it is achieved by allowing the parsing engine to trigger events that can also be processed as extended functions. The work presented herewith is divided into three parts: In the first part, we study the principles regarding the construction of the extended local grammars. Then, we present a proof-of-concept of a corpus-processing tool which implements the proposed formalism. Finally, we study some techniques to extract information from both well-formed and noisy texts. We focus on the coupling of external resources and non-symbolic methods in the construction of our grammars and we highlight the suitability of this approach in order to overcome the inherent limitations of classical local grammars
Zaslavskiy, Mikhail. "L'alignement de graphes : applications en bioinformatique et vision par ordinateur." Phd thesis, École Nationale Supérieure des Mines de Paris, 2010. http://pastel.archives-ouvertes.fr/pastel-00006121.
Повний текст джерелаKalainathan, Diviyan. "Generative Neural Networks to infer Causal Mechanisms : algorithms and applications." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS516.
Повний текст джерелаCausal discovery is of utmost importance for agents who must plan, reason and decide based on observations; where mistaking correlation with causation might lead to unwanted consequences. The gold standard to discover causal relations is to perform experiments.However, experiments are in many cases expensive, unethical, or impossible to realize. In these situations, there is a need for observational causal discovery, that is, the estimation of causal relations from observations alone.Causal discovery in the observational data setting traditionally involves making significant assumptions on the data and on the underlying causal model.This thesis aims to alleviate some of the assumptions made on the causal models by exploiting the modularity and expressiveness of neural networks for causal discovery, leveraging both conditional independences and simplicity of the causal mechanisms through two algorithms.Extensive experiments on both simulated and real-world data and a throughout theoretical anaylsis prove the good performance and the soundness of the proposed approaches
Allart, Thibault. "Apprentissage statistique sur données longitudinales de grande taille et applications au design des jeux vidéo." Thesis, Paris, CNAM, 2017. http://www.theses.fr/2017CNAM1136/document.
Повний текст джерелаThis thesis focuses on longitudinal time to event data possibly large along the following tree axes : number of individuals, observation frequency and number of covariates. We introduce a penalised estimator based on Cox complete likelihood with data driven weights. We introduce proximal optimization algorithms to efficiently fit models coefficients. We have implemented thoses methods in C++ and in the R package coxtv to allow everyone to analyse data sets bigger than RAM; using data streaming and online learning algorithms such that proximal stochastic gradient descent with adaptive learning rates. We illustrate performances on simulations and benchmark with existing models. Finally, we investigate the issue of video game design. We show that using our model on large datasets available in video game industry allows us to bring to light ways of improving the design of studied games. First we have a look at low level covariates, such as equipment choices through time and show that this model allows us to quantify the effect of each game elements, giving to designers ways to improve the game design. Finally, we show that the model can be used to extract more general design recommendations such as dificulty influence on player motivations
Jabiri, Fouad. "Applications de méthodes de classification non supervisées à la détection d'anomalies." Master's thesis, Université Laval, 2020. http://hdl.handle.net/20.500.11794/67914.
Повний текст джерелаIn this thesis, we will first present the binary tree partitioning algorithm and isolation forests. Binary trees are very popular classifiers in supervised machine learning. The isolation forest belongs to the family of unsupervised methods. It is an ensemble of binary trees used in common to isolate outlying instances. Subsequently, we will present the approach that we have named "Exponential smoothig" (or "pooling"). This technique consists in encoding sequences of variables of different lengths into a single vector of fixed size. Indeed, the objective of this thesis is to apply the algorithm of isolation forests to identify anomalies in insurance claim forms available in the database of a large Canadian insurance company in order to detect cases of fraud. However, a form is a sequence of claims. Each claim is characterized by a set of variables and thus it will be impossible to apply the isolation forest algorithm directly to this kind of data. It is for this reason that we are going to apply Exponential smoothing. Our application effectively isolates claims and abnormal forms, and we find that the latter tend to be audited by the company more often than regular forms.
El, Hatib Souad. "Une approche sémantique de détection de maliciel Android basée sur la vérification de modèles et l'apprentissage automatique." Master's thesis, Université Laval, 2020. http://hdl.handle.net/20.500.11794/66322.
Повний текст джерелаThe ever-increasing number of Android malware is accompanied by a deep concern about security issues in the mobile ecosystem. Unquestionably, Android malware detection has received much attention in the research community and therefore it becomes a crucial aspect of software security. Actually, malware proliferation goes hand in hand with the sophistication and complexity of malware. To illustrate, more elaborated malware like polymorphic and metamorphic malware, make use of code obfuscation techniques to build new variants that preserve the semantics of the original code but modify it’s syntax and thus escape the usual detection methods. In the present work, we propose a model-checking based approach that combines static analysis and machine learning. Mainly, from a given Android application we extract an abstract model expressed in terms of LNT, a process algebra language. Afterwards, security related Android behaviours specified by temporal logic formulas are checked against this model, the satisfaction of a specific formula is considered as a feature, finally machine learning algorithms are used to classify the application as malicious or not.
Varelas, Konstantinos. "Randomized Derivative Free Optimization via CMA-ES and Sparse Techniques : Applications to Radars." Thesis, Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAX012.
Повний текст джерелаIn this thesis, we investigate aspects of adaptive randomized methods for black-box continuous optimization. The algorithms that we study are based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm and focus on large scale optimization problems.We start with a description of CMA-ES and its relation to the Information Geometric Optimization (IGO) framework, succeeded by a comparative study of large scale variants of CMA-ES. We furthermore propose novel methods which integrate tools of high dimensional analysis within CMA-ES, to obtain more efficient algorithms for large scale partially separable problems.Additionally, we describe the methodology for algorithm performance evaluation adopted by the Comparing Continuous Optimizers (COCO) platform, and finalize the bbob-largescale test suite, a novel benchmarking suite with problems of increased dimensions and with a low computational cost.Finally, we present the formulation, methodology and obtained results for two applications related to Radar problems, the Phase Code optimization problem and the Phased-Array Pattern design problem
Grumbach, Alain. "Contribution à l'étude de modèles d'apprentissage en interaction avec un environnement inspirés du comportement humain." Paris 11, 1987. http://www.theses.fr/1987PA112244.
Повний текст джерелаThe aim of this research is to design and write programs which provide machines with learning by doing capabilities, in a problem solving situation. The approach is inspired by human behavior study : subject observations, model building, drawing out of fundamental notions, design of corresponding software (language programs). The fundamental notions set out in this work concern : knowledge types and relationship, which gave rise to the knowledge Lattice structure that relates general and specific knowledge fields to one another ; learning behavior trigerring : on event ; acquired informations : links between them,. . . The language built up from these notions is twofold : Multilog, implementation of the Knowledge Lattice within Logic Programing through clause "worlds" ; Evenlog, implementation of the event notion. Different human learning simulation programs have been developed, ranging from a specific model to a very general one whose unreached objective was to be able to learn non specific knowledge fields. Lastly, this work, as part of Cognitive Science Research, proposes a short Cogitation on Knowledge : the above mentionned Knowledge Lattice ; different points of view : behaviorist, cognitive,. . . , links between them ; (phylo) genesis
Dupuy, Christophe. "Inference and applications for topic models." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE055/document.
Повний текст джерелаMost of current recommendation systems are based on ratings (i.e. numbers between 0 and 5) and try to suggest a content (movie, restaurant...) to a user. These systems usually allow users to provide a text review for this content in addition to ratings. It is hard to extract useful information from raw text while a rating does not contain much information on the content and the user. In this thesis, we tackle the problem of suggesting personalized readable text to users to help them make a quick decision about a content. More specifically, we first build a topic model that predicts personalized movie description from text reviews. Our model extracts distinct qualitative (i.e., which convey opinion) and descriptive topics by combining text reviews and movie ratings in a joint probabilistic model. We evaluate our model on an IMDB dataset and illustrate its performance through comparison of topics. We then study parameter inference in large-scale latent variable models, that include most topic models. We propose a unified treatment of online inference for latent variable models from a non-canonical exponential family, and draw explicit links between several previously proposed frequentist or Bayesian methods. We also propose a novel inference method for the frequentist estimation of parameters, that adapts MCMC methods to online inference of latent variable models with the proper use of local Gibbs sampling.~For the specific latent Dirichlet allocation topic model, we provide an extensive set of experiments and comparisons with existing work, where our new approach outperforms all previously proposed methods. Finally, we propose a new class of determinantal point processes (DPPs) which can be manipulated for inference and parameter learning in potentially sublinear time in the number of items. This class, based on a specific low-rank factorization of the marginal kernel, is particularly suited to a subclass of continuous DPPs and DPPs defined on exponentially many items. We apply this new class to modelling text documents as sampling a DPP of sentences, and propose a conditional maximum likelihood formulation to model topic proportions, which is made possible with no approximation for our class of DPPs. We present an application to document summarization with a DPP on 2 to the power 500 items, where the summaries are composed of readable sentences
Bussy, Simon. "Introduction of high-dimensional interpretable machine learning models and their applications." Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS488.
Повний текст джерелаThis dissertation focuses on the introduction of new interpretable machine learning methods in a high-dimensional setting. We developped first the C-mix, a mixture model of censored durations that automatically detects subgroups based on the risk that the event under study occurs early; then the binarsity penalty combining a weighted total variation penalty with a linear constraint per block, that applies on one-hot encoding of continuous features; and finally the binacox model that uses the binarsity penalty within a Cox model to automatically detect cut-points in the continuous features. For each method, theoretical properties are established: algorithm convergence, non-asymptotic oracle inequalities, and comparison studies with state-of-the-art methods are carried out on both simulated and real data. All proposed methods give good results in terms of prediction performances, computing time, as well as interpretability abilities
Mourad, Raphaël. "Modélisation pangénomique du déséquilibre de liaison à l'aide de réseaux bayésiens hiérarchiques latents et applications." Phd thesis, Université de Nantes, 2011. http://tel.archives-ouvertes.fr/tel-00628759.
Повний текст джерелаSchutz, Georges. "Adaptations et applications de modèles mixtes de réseaux de neurones à un processus industriel." Phd thesis, Université Henri Poincaré - Nancy I, 2006. http://tel.archives-ouvertes.fr/tel-00115770.
Повний текст джерелаartificiels pour améliorer le contrôle de processus industriels
complexes, caractérisés en particulier par leur aspect temporel.
Les motivations principales pour traiter des séries temporelles
sont la réduction du volume de données, l'indexation pour la
recherche de similarités, la localisation de séquences,
l'extraction de connaissances (data mining) ou encore la
prédiction.
Le processus industriel choisi est un four à arc
électrique pour la production d'acier liquide au Luxembourg. Notre
approche est un concept de contrôle prédictif et se base sur des
méthodes d'apprentissage non-supervisé dans le but d'une
extraction de connaissances.
Notre méthode de codage se base sur
des formes primitives qui composent les signaux. Ces formes,
composant un alphabet de codage, sont extraites par une méthode
non-supervisée, les cartes auto-organisatrices de Kohonen (SOM).
Une méthode de validation des alphabets de codage accompagne
l'approche.
Un sujet important abordé durant ces recherches est
la similarité de séries temporelles. La méthode proposée est
non-supervisée et intègre la capacité de traiter des séquences de
tailles variées.
Mederreg, Lotfi. "Etude cinématique et reproduction robotique de la marche chez l'oiseau." Versailles-St Quentin en Yvelines, 2006. http://www.theses.fr/2006VERS0043.
Повний текст джерелаThis work deals with the kinematics study of bird-like robot locomotion capabilities. An experimental protocol has been developed in collaboration with the researchers of the Natural History Museum of Paris. The experiments consist of filming trained quails walking on a specific walk-way trough an X-ray camera. Kinematics data are recorded and processed with special mathematical tools developed for this purpose. The analysis of the results allow to identify several characteristic leg configurations of the walking cycle, make some assumptions concerning the kinematics of the movement and propose a geometrical model for the bird-like structure. In order to develop control laws, and because of the redundancy of the structure, some optimization criteria are defined to help solve the inverse geometrical model. Finally, some control strategies are developed to reproduce the walking pattern for robotics applications
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
Hocking, Toby Dylan. "Learning algorithms and statistical software, with applications to bioinformatics." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00906029.
Повний текст джерелаJenatton, Rodolphe. "Structured sparsity-inducing norms : statistical and algorithmic properties with applications to neuroimaging." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2011. http://tel.archives-ouvertes.fr/tel-00668379.
Повний текст джерела