Auswahl der wissenschaftlichen Literatur zum Thema „Algorithmes ensemblistes“
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Dissertationen zum Thema "Algorithmes ensemblistes"
Frery, Jordan. „Ensemble Learning for Extremely Imbalced Data Flows“. Thesis, Lyon, 2019. http://www.theses.fr/2019LYSES034.
Der volle Inhalt der QuelleMachine learning is the study of designing algorithms that learn from trainingdata to achieve a specific task. The resulting model is then used to predict overnew (unseen) data points without any outside help. This data can be of manyforms such as images (matrix of pixels), signals (sounds,...), transactions (age,amount, merchant,...), logs (time, alerts, ...). Datasets may be defined to addressa specific task such as object recognition, voice identification, anomaly detection,etc. In these tasks, the knowledge of the expected outputs encourages a supervisedlearning approach where every single observed data is assigned to a label thatdefines what the model predictions should be. For example, in object recognition,an image could be associated with the label "car" which suggests that the learningalgorithm has to learn that a car is contained in this picture, somewhere. This is incontrast with unsupervised learning where the task at hand does not have explicitlabels. For example, one popular topic in unsupervised learning is to discoverunderlying structures contained in visual data (images) such as geometric formsof objects, lines, depth, before learning a specific task. This kind of learning isobviously much harder as there might be potentially an infinite number of conceptsto grasp in the data. In this thesis, we focus on a specific scenario of thesupervised learning setting: 1) the label of interest is under represented (e.g.anomalies) and 2) the dataset increases with time as we receive data from real-lifeevents (e.g. credit card transactions). In fact, these settings are very common inthe industrial domain in which this thesis takes place
Koco, Sokol. „Méthodes ensembliste pour des problèmes de classification multi-vues et multi-classes avec déséquilibres“. Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4101/document.
Der volle Inhalt der QuelleNowadays, in many fields, such as bioinformatics or multimedia, data may be described using different sets of features, also called views. For a given classification task, we distinguish two types of views:strong views, which are suited for the task, and weak views suited for a (small) part of the task; in multi-class learning, a view can be strong with respect to some (few) classes and weak for the rest of the classes: these are imbalanced views. The works presented in this thesis fall in the supervised learning setting and their aim is to address the problem of multi-view learning under strong, weak and imbalanced views, regrouped under the notion of uneven views. The first contribution of this thesis is a multi-view learning algorithm based on the same framework as AdaBoost.MM. The second part of this thesis proposes a unifying framework for imbalanced classes supervised methods (some of the classes are more represented than others). In the third part of this thesis, we tackle the uneven views problem through the combination of the imbalanced classes framework and the between-views cooperation used to take advantage of the multiple views. In order to test the proposed methods on real-world data, we consider the task of phone calls classifications, which constitutes the subject of the ANR DECODA project. Each part of this thesis deals with different aspects of the problem
Cherifa-Luron, Ményssa. „Prédiction des épisodes d'hypotension à partir de données longitudinales à haute fréquence recueillies auprès de patients en soins intensifs“. Electronic Thesis or Diss., Université Paris Cité, 2021. https://wo.app.u-paris.fr/cgi-bin/WebObjects/TheseWeb.woa/wa/show?t=8076&f=67992.
Der volle Inhalt der QuelleThe digital revolution in healthcare, reflected in both the centralization of and access to extensive medical databases and the considerable advances in artificial intelligence (AI), has created new opportunities for data science applied to medicine. Putting the patient at the heart of the health care system, developing these new technologies guarantees a more personalized medicine by identifying more predictive factors and individual prognosis. This thesis work is entirely in line with the concept of personalized medicine. More precisely, it is an example of medical AI's development and concrete application to predict hypotension and, more broadly, of states of shock, frequent pathologies affecting more than one-third of patients hospitalized in intensive care. Indeed, shock, defined as a failure of the circulatory system leading to an inadequacy between the supply and the peripheral tissue needs in oxygen, is considered a diagnostic and therapeutic emergency. Therefore, anticipating hypotension, one of its main symptoms, can be extremely useful to make better therapeutic decisions and, in some cases, prevent the onset of organ failure from the beginning by appropriately adjusting the therapy. In addition, the ability to predict future deterioration can be beneficial to assist in the proactive assignment of care teams within hospital departments. The first part of this thesis work focused on using and applying a machine learning-based ensemble algorithm, the Super Learner (SL), to predict the occurrence of a hypotensive episode 10 minutes or more in advance in patients hospitalized in the ICU. This work demonstrated that physiological signals could be integrated into predictive models when dealing with massive data without requiring complex pre-processing methods to be exploited. Also, the SL was far superior to each of the algorithms included in its library, as evidenced by its lower errors and good values of sensitivity and specificity values during its internal and external evaluation. Then, to mimic the way that clinicians are trained to jointly analyze the evolution of mean arterial pressure (MAP) and heart rate (HR) given their close physiological interdependence, we developed a deep learning model, the Physiological Deep Learner (PDL), to predict MAP and HR simultaneously. We highlighted that the use of a multitasking algorithm outperformed the prediction performance of single-tasking algorithms. Indeed, compared to a more traditional approach, our PDL achieved better performance, exhibiting a better calibration profile and fewer errors. In addition, the PDL was able to predict with high accuracy the occurrence or non-occurrence of a hypotensive episode up to 60 minutes in advance
Lassoued, Khaoula. „Localisation de robots mobiles en coopération mutuelle par observation d'état distribuée“. Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2289/document.
Der volle Inhalt der QuelleIn this work, we study some cooperative localization issues for mobile robotic systems that interact with each other without using relative measurements (e.g. bearing and relative distances). The considered localization technologies are based on beacons or satellites that provide radio-navigation measurements. Such systems often lead to offsets between real and observed positions. These systematic offsets (i.e, biases) are often due to inaccurate beacon positions, or differences between the real electromagnetic waves propagation and the observation models. The impact of these biases on robots localization should not be neglected. Cooperation and data exchange (estimates of biases, estimates of positions and proprioceptive measurements) reduce significantly systematic errors. However, cooperative localization based on sharing estimates is subject to data incest problems (i.e, reuse of identical information in the fusion process) that often lead to over-convergence problems. When position information is used in a safety-critical context (e.g. close navigation of autonomous robots), one should check the consistency of the localization estimates. In this context, we aim at characterizing reliable confidence domains that contain robots positions with high reliability. Hence, set-membership methods are considered as efficient solutions. This kind of approach enables merging adequately the information even when it is reused several time. It also provides reliable domains. Moreover, the use of non-linear models does not require any linearization. The modeling of a cooperative system of nr robots with biased beacons measurements is firstly presented. Then, we perform an observability study. Two cases regarding the localization technology are considered. Observability conditions are identified and demonstrated. We then propose a set-membership method for cooperativelocalization. Cooperation is performed by sharing estimated positions, estimated biases and proprioceptive measurements. Sharing biases estimates allows to reduce the estimation error and the uncertainty of the robots positions. The algorithm feasibility is validated through simulation when the observations are beacons distance measurements with several robots. The cooperation provides better performance compared to a non-cooperative method. Afterwards, the cooperative algorithm based on set-membership method is tested using real data with two experimental vehicles. Finally, we compare the interval method performance with a sequential Bayesian approach based on covariance intersection. Experimental results indicate that the interval approach provides more accurate positions of the vehicles with smaller confidence domains that remain reliable. Indeed, the comparison is performed in terms of accuracy and uncertainty
Lalami, Abdelhalim. „Diagnostic et approches ensemblistes à base de zonotopes“. Cergy-Pontoise, 2008. http://biblioweb.u-cergy.fr/theses/08CERG0377.pdf.
Der volle Inhalt der QuelleFault diagnosis consists in detecting, isolating and possibly identifying the faults occurring in a system. As a model never perfectly represent the reality, the uncertainties have to be explicitly formalized in order to implement analytical redundancy approaches providing a guaranteed diagnosis. Based on a deterministic representation of uncertainties (by intervals and, more precisely, by zonotopes, a particular class of polytopes), this work follows two main objectives: proposing a specification of operating modes which is as close as possible to the available knowledge, and ensuring the logical soundness between the specification of the operating modes and the diagnosis decision. Using reachability algorithms based on zonotopes to control the dependency problem and the wrapping effect, on the one hand, using collision detection algorithms, on the other hand, the interest in a setmembership re-formulation of several residual generation methods is put into evidence not only to design on-line tests, but also to design and analyse the properties of a fault diagnosis system (adjustment of thresholds, sensitivity analysis). Set-membership approaches allow to introduce the notion of decoupling in the limits fixed by some bounds An arbitrary number of perturbations can then be perfectly decoupled without any rank constraint. The computed domains allow to bound the uncertainties in all the space directions and so obtain better sensitivities than those resulting from projective or elimination approaches. The work about reachability computations has lead to developments that are expected to be useful for the verification of safety properties of hybrid dynamical systems
Dandach, Hoda. „Prédiction de l'espace navigable par l'approche ensembliste pour un véhicule routier“. Thesis, Compiègne, 2014. http://www.theses.fr/2014COMP1892/document.
Der volle Inhalt der QuelleIn this thesis, we aim to characterize a vehicle stable state domain, as well as vehicle state estimation, using interval methods.In the first part of this thesis, we are interested in the intelligent vehicle state estimation.The Bayesian approach is one of the most popular and used approaches of estimation. It is based on the calculated probability of the density function which is neither evident nor simple all the time, conditioned on the available measurements.Among the Bayesian approaches, we know the Kalman filter (KF) in its three forms(linear, non linear and unscented). All the Kalman filters assume unimodal Gaussian state and measurement distributions. As an alternative, the Particle Filter(PF) is a sequential Monte Carlo Bayesian estimator. Contrary to Kalman filter,PF is supposed to give more information about the posterior even when it has a multimodal shape or when the noise follows non-Gaussian distribution. However,the PF is very sensitive to the imprecision due by bias or noise, and its efficiency and accuracy depend mainly on the number of propagated particles which can easily and significantly increase as a result of this imprecision. In this part, we introduce the interval framework to deal with the problems of the non-white biased measurements and bounded errors. We use the Box Particle Filter (BPF), an estimator based simultaneously on the interval analysis and on the particle approach. We aim to estimate some immeasurable state from the vehicle dynamics using the bounded error Box Particle algorithm, like the roll angle and the lateral load transfer, which are two dynamic states of the vehicle. BPF gives a guaranteed estimation of the state vector. The box encountering the estimation is guaranteed to encounter thereal value of the estimated variable as well.In the second part of this thesis, we aim to compute a vehicle stable state domain.An algorithm, based on the set inversion principle and the constraints satisfaction,is used. Considering the longitudinal velocity and the side slip angle at the vehicle centre of gravity, we characterize the set of these two state variables that corresponds to a stable behaviour : neither roll-over nor sliding. Concerning the roll-over risk,we use the lateral transfer ratio LTR as a risk indicator. Concerning the sliding risk, we use the wheels side slip angles. All these variables are related geometrically to the longitudinal velocity and the side slip angle at the centre of gravity. Using these constraints, the set inversion principle is applied in order to define the set ofthe state variables where the two mentioned risks are avoided. The algorithm of Sivia is implemented. Knowing the vehicle trajectory, a maximal allowed velocityon every part of this trajectory is deduced
Vincke, Bastien. „Architectures pour des systèmes de localisation et de cartographie simultanées“. Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00770323.
Der volle Inhalt der QuelleRaharjo, Agus Budi. „Reliability in ensemble learning and learning from crowds“. Electronic Thesis or Diss., Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0606.
Der volle Inhalt der QuelleThe combination of several human expert labels is generally used to make reliable decisions. However, using humans or learning systems to improve the overall decision is a crucial problem. Indeed, several human experts or machine learning have not necessarily the same performance. Hence, a great effort is made to deal with this performance problem in the presence of several actors, i.e., humans or classifiers. In this thesis, we present the combination of reliable classifiers in ensemble learning and learning from crowds. The first contribution is a method, based on weighted voting, which allows selecting a reliable combination of classifications. Our algorithm RelMV transforms confidence scores, obtained during the training phase, into reliable scores. By using these scores, it determines a set of reliable candidates through both static and dynamic selection process. When it is hard to find expert labels as ground truth, we propose an approach based on Bayesian and expectation-maximization (EM) as our second contribution. The aim is to evaluate the reliability degree of each annotator and to aggregate the appropriate labels carefully. We optimize the computation time of the algorithm in order to adapt a large number of data collected from crowds. The obtained outcomes show better accuracy, stability, and computation time compared to the previous methods. Also, we conduct an experiment considering the melanoma diagnosis problem using a real-world medical dataset consisting of a set of skin lesions images, which is annotated by multiple dermatologists
Tran, Dinh Khoi Quoc. „Contributions à l'identification enesembliste ellipsoïdale“. Phd thesis, 2005. http://tel.archives-ouvertes.fr/tel-00168416.
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