Literatura académica sobre el tema "Apprentissage d'ensemble"
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Tesis sobre el tema "Apprentissage d'ensemble"
Guo, Li. "Classifieurs multiples intégarnt la marge d'ensemble. Application aux données de télédétection". Bordeaux 3, 2011. http://www.theses.fr/2011BOR30022.
Texto completoThis dissertation focuses on exploiting the ensemble margin concept to design better ensemble classifiers. Some training data set issues, such as redundancy, imbalanced classes and noise, are investigated in an ensemble margin framework. An alternative definition of the ensemble margin is at the core of this work. An innovative approach to measure the importance of each instance in the learning process is introduced. We show that there is less redundancy among smaller margin instances than among higher margin ones. In addition, these smaller margin instances carry more significant information than higher margin instances. Therefore, these low margin instances have a major influence in forming an appropriate training set to build up a reliable classifier. Based on these observations, we propose a new boundary bagging method. Another major issue that is investigated in this thesis is the complexity induced by an ensemble approach which usually involves a significant number of base classifiers. A new efficient ensemble pruning method is proposed. It consists in ordering all the base classifiers with respect to an entropy-inspired criterion that also exploits our new version of the margin of ensemble methods. Finally, the proposed ensemble methods are applied to remote sensing data analysis at three learning levels: data level, feature level and classifier level
Roy, Jean-Francis. "Apprentissage automatique avec garanties de généralisation à l'aide de méthodes d'ensemble maximisant le désaccord". Doctoral thesis, Université Laval, 2018. http://hdl.handle.net/20.500.11794/29563.
Texto completoWe focus on machine learning, a branch of artificial intelligence. When solving a classification problem, a learning algorithm is provided labelled data and has the task of learning a function that will be able to automatically classify future, unseen data. Many classical learning algorithms are designed to combine simple classifiers by building a weighted majority vote classifier out of them. In this thesis, we extend the usage of the C-bound, bound on the risk of the majority vote classifier. This bound is defined using two quantities : the individual performance of the voters, and the correlation of their errors (their disagreement). First, we design majority vote generalization bounds based on the C-bound. Then, we extend this bound from binary classification to generalized majority votes. Finally, we develop new learning algorithms with state-of-the-art performance, by constructing majority votes that maximize the voters’ disagreement, while controlling their individual performance. The generalization guarantees that we develop in this thesis are in the family of PAC-Bayesian bounds. We generalize the PAC-Bayesian theory by introducing a general theorem, from which the classical bounds from the literature can be recovered. Using this same theorem, we introduce generalization bounds based on the C-bound. We also simplify the proof process of PAC-Bayesian theorems, easing the development of new families of bounds. We introduce two new families of PAC-Bayesian bounds. One is based on a different notion of complexity than usual bounds, the Rényi divergence, instead of the classical Kullback-Leibler divergence. The second family is specialized to transductive learning, instead of inductive learning. The two learning algorithms that we introduce, MinCq and CqBoost, output a majority vote classifier that maximizes the disagreement between voters. An hyperparameter of the algorithms gives a direct control over the individual performance of the voters. These two algorithms being designed to minimize PAC-Bayesian generalization bounds on the risk of the majority vote classifier, they come with rigorous theoretical guarantees. By performing an empirical evaluation, we show that MinCq and CqBoost perform as well as classical stateof- the-art algorithms.
Baudin, Paul. "Prévision séquentielle par agrégation d'ensemble : application à des prévisions météorologiques assorties d'incertitudes". Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLS117/document.
Texto completoIn this thesis, we study sequential prediction problems. The goal is to devise and apply automatic strategy, learning from the past, with potential help from basis predictors. We desire these strategies to have strong mathematical guarantees and to be valid in the most general cases. This enables us to apply the algorithms deriving from the strategies to meteorological data predictions. Finally, we are interested in theoretical and practical versions of this sequential prediction framework to cumulative density function prediction. Firstly, we study online prediction of bounded stationary ergodic processes. To do so, we consider the setting of prediction of individual sequences and propose a deterministic regression tree that performs asymptotically as well as the best L-Lipschitz predictor. Then, we show why the obtained regret bound entails the asymptotical optimality with respect to the class of bounded stationary ergodic processes. Secondly, we propose a specific sequential aggregation method of meteorological simulation of mean sea level pressure. The aim is to obtain, with a ridge regression algorithm, better prediction performance than a reference prediction, belonging to the constant linear prediction of basis predictors. We begin by recalling the mathematical framework and basic notions of environmental science. Then, the used datasets and practical performance of strategies are studied, as well as the sensitivity of the algorithm to parameter tuning. We then transpose the former method to another meteorological variable: the wind speed 10 meter above ground. This study shows that the wind speed exhibits different behaviors on a macro level. In the last chapter, we present the tools used in a probabilistic prediction framework and underline their merits. First, we explain the relevancy of probabilistic prediction and expose this domain's state of the art. We carry on with an historical approach of popular probabilistic scores. The used algorithms are then thoroughly described before the descriptions of their empirical results on the mean sea level pressure and wind speed
Loth, Manuel. "Algorithmes d'Ensemble Actif pour le LASSO". Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2011. http://tel.archives-ouvertes.fr/tel-00845441.
Texto completoTran, Anh-Tuan. "Ensemble learning-based approach for the global minimum variance portfolio". Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLP010.
Texto completoEnsemble Learning has a simple idea that combining several learning algorithms tend to yield a better result than any single learning algorithm. Empirically, the ensemble method is better if its base models are diversified even if they are non-intuitively random algorithms such as random decision trees. Because of its advantages, Ensemble Learning is used in various applications such as fraud detection problems. In more detail, the advantages of Ensemble Learning are because of two points: i) combines the strengths of its base models then each model is complementary to one another and ii) neutralizes the noise and outliers among all base models then reduces their impacts on the final predictions. We use these two ideas of Ensemble Learning for different applications in the Machine Learning and the Finance industry. Our main contributions in this thesis are: i) efficiently deal with a hard scenario of imbalance data problem in the Machine Learning which is extremely imbalance big data problem by using undersampling technique and the Ensemble Learning, ii) appropriately apply time-series Cross-Validation and the Ensemble Learning to resolve a covariance matrix estimator selection problem in Quantitative Trading and iii) reduce the impact of outliers in covariance matrix estimations in order to increase the stability of portfolios by using the undersampling and the Ensemble Learning
Thorey, Jean. "Prévision d’ensemble par agrégation séquentielle appliquée à la prévision de production d’énergie photovoltaïque". Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066526/document.
Texto completoOur main objective is to improve the quality of photovoltaic power forecasts deriving from weather forecasts. Such forecasts are imperfect due to meteorological uncertainties and statistical modeling inaccuracies in the conversion of weather forecasts to power forecasts. First we gather several weather forecasts, secondly we generate multiple photovoltaic power forecasts, and finally we build linear combinations of the power forecasts. The minimization of the Continuous Ranked Probability Score (CRPS) allows to statistically calibrate the combination of these forecasts, and provides probabilistic forecasts under the form of a weighted empirical distribution function. We investigate the CRPS bias in this context and several properties of scoring rules which can be seen as a sum of quantile-weighted losses or a sum of threshold-weighted losses. The minimization procedure is achieved with online learning techniques. Such techniques come with theoretical guarantees of robustness on the predictive power of the combination of the forecasts. Essentially no assumptions are needed for the theoretical guarantees to hold. The proposed methods are applied to the forecast of solar radiation using satellite data, and the forecast of photovoltaic power based on high-resolution weather forecasts and standard ensembles of forecasts
Jaber, Ghazal. "An approach for online learning in the presence of concept changes". Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00907486.
Texto completoBoulegane, Dihia. "Machine learning algorithms for dynamic Internet of Things". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT048.
Texto completoWith the rapid growth of Internet-of-Things (IoT) devices and sensors, sources that are continuously releasing and curating vast amount of data at high pace in the form of stream. The ubiquitous data streams are essential for data driven decisionmaking in different business sectors using Artificial Intelligence (AI) and Machine Learning (ML) techniques in order to extract valuable knowledge and turn it to appropriate actions. Besides, the data being collected is often associated with a temporal indicator, referred to as temporal data stream that is a potentially infinite sequence of observations captured over time at regular intervals, but not necessarily. Forecasting is a challenging tasks in the field of AI and aims at understanding the process generating the observations over time based on past data in order to accurately predict future behavior. Stream Learning is the emerging research field which focuses on learning from infinite and evolving data streams. The thesis tackles dynamic model combination that achieves competitive results despite their high computational costs in terms of memory and time. We study several approaches to estimate the predictive performance of individual forecasting models according to the data and contribute by introducing novel windowing and meta-learning based methods to cope with evolving data streams. Subsequently, we propose different selection methods that aim at constituting a committee of accurate and diverse models. The predictions of these models are then weighted and aggregated. The second part addresses model compression that aims at building a single model to mimic the behavior of a highly performing and complex ensemble while reducing its complexity. Finally, we present the first streaming competition ”Real-time Machine Learning Competition on Data Streams”, at the IEEE Big Data 2019 conference, using the new SCALAR platform
Tremblay, Guillaume. "Optimisation d'ensembles de classifieurs non paramétriques avec apprentissage par représentation partielle de l'information". Mémoire, École de technologie supérieure, 2004. http://espace.etsmtl.ca/716/1/TREMBLAY_Guillaume.pdf.
Texto completoFaddoul, Jean Baptiste. "Modèles d'Ensembles pour l'Apprentissage Multi-Tache, avec des taches Hétérogènes et sans Restrictions". Phd thesis, Université Charles de Gaulle - Lille III, 2012. http://tel.archives-ouvertes.fr/tel-00712710.
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