Literatura científica selecionada sobre o tema "Clustering sous contraintes"
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Teses / dissertações sobre o assunto "Clustering sous contraintes"
Ouali, Abdelkader. "Méthodes hybrides parallèles pour la résolution de problèmes d'optimisation combinatoire : application au clustering sous contraintes". Thesis, Normandie, 2017. http://www.theses.fr/2017NORMC215/document.
Texto completo da fonteCombinatorial optimization problems have become the target of many scientific researches for their importance in solving academic problems and real problems encountered in the field of engineering and industry. Solving these problems by exact methods is often intractable because of the exorbitant time processing that these methods would require to reach the optimal solution(s). In this thesis, we were interested in the algorithmic context of solving combinatorial problems, and the modeling context of these problems. At the algorithmic level, we have explored the hybrid methods which excel in their ability to cooperate exact methods and approximate methods in order to produce rapidly solutions of best quality. At the modeling level, we worked on the specification and the exact resolution of complex problems in pattern set mining, in particular, by studying scaling issues in large databases. On the one hand, we proposed a first parallelization of the DGVNS algorithm, called CPDGVNS, which explores in parallel the different clusters of the tree decomposition by sharing the best overall solution on a master-worker model. Two other strategies, called RADGVNS and RSDGVNS, have been proposed which improve the frequency of exchanging intermediate solutions between the different processes. Experiments carried out on difficult combinatorial problems show the effectiveness of our parallel methods. On the other hand, we proposed a hybrid approach combining techniques of both Integer Linear Programming (ILP) and pattern mining. Our approach is comprehensive and takes advantage of the general ILP framework (by providing a high level of flexibility and expressiveness) and specialized heuristics for data mining (to improve computing time). In addition to the general framework for the pattern set mining, two problems were studied: conceptual clustering and the tiling problem. The experiments carried out showed the contribution of our proposition in relation to constraint-based approaches and specialized heuristics
Schild, Erwan. "De l’importance de valoriser l’expertise humaine dans l’annotation : application à la modélisation de textes en intentions à l’aide d’un clustering interactif". Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0024.
Texto completo da fonteUsually, the task of annotation, used to train conversational assistants, relies on domain experts who understand the subject matter to model. However, data annotation is known to be a challenging task due to its complexity and subjectivity. Therefore, it requires strong analytical skills to model the text in dialogue intention. As a result, most annotation projects choose to train experts in analytical tasks to turn them into "super-experts". In this thesis, we decided instead to focus on the real knowledge of experts by proposing a new annotation method based on Interactive Clustering. This method involves a Human-Machine cooperation, where the machine performs clustering to provide an initial learning base, and the expert annotates MUST-LINK or CANNOT-LINK constraints between the data to iteratively refine the proposed learning base. Such annotation has the advantage of being more instinctive, as experts can associate or differentiate data according to the similarity of their use cases, allowing them to handle the data as they would professionally do on a daily basis. During our studies, we have been able to show that this method significantly reduces the complexity of designing a learning base, notably by reducing the need for training the experts involved in an annotation project. We provide a technical implementation of this method (algorithms and associated graphical interface), as well as a study of optimal parameters to achieve a coherent learning base with minimal annotation. We have also conducted a cost study (both technical and human) to confirm that the use of such a method is realistic in an industrial context. Finally, we provide a set of recommendations to help this method reach its full potential, including: (1) advice aimed at framing the annotation strategy, (2) assistance in identifying and resolving differences of opinion between annotators, (3) rentability indicators for each expert intervention, and (4) methods for analyzing the relevance of the learning base under construction. In conclusion, this thesis provides an innovative approach to design a learning base for a conversational assistant, involving domain experts for their actual knowledge, while requiring a minimum of analytical and technical skills. This work opens the way for more accessible methods for building such assistants
Makkhongkaew, Raywat. "Semi-supervised co-selection : instances and features : application to diagnosis of dry port by rail". Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE1341.
Texto completo da fonteWe are drowning in massive data but starved for knowledge retrieval. It is well known through the dimensionality tradeoff that more data increase informative but pay a price in computational complexity, which has to be made up in some way. When the labeled sample size is too little to bring sufficient information about the target concept, supervised learning fail with this serious challenge. Unsupervised learning can be an alternative in this problem. However, as these algorithms ignore label information, important hints from labeled data are left out and this will generally downgrades the performance of unsupervised learning algorithms. Using both labeled and unlabeled data is expected to better procedure in semi-supervised learning, which is more adapted for large domain applications when labels are hardly and costly to obtain. In addition, when data are large, feature selection and instance selection are two important dual operations for removing irrelevant information. Both of tasks with semisupervised learning are different challenges for machine learning and data mining communities for data dimensionality reduction and knowledge retrieval. In this thesis, we focus on co-selection of instances and features in the context of semi-supervised learning. In this context, co-selection becomes a more challenging problem as the data contains labeled and unlabeled examples sampled from the same population. To do such semi-supervised coselection, we propose two unified frameworks, which efficiently integrate labeled and unlabeled parts into the co-selection process. The first framework is based on weighting constrained clustering and the second one is based on similarity preserving selection. Both approaches evaluate the usefulness of features and instances in order to select the most relevant ones, simultaneously. Finally, we present a variety of empirical studies over high-dimensional data sets, which are well-known in the literature. The results are promising and prove the efficiency and effectiveness of the proposed approaches. In addition, the developed methods are validated on a real world application, over data provided by the State Railway of Thailand (SRT). The purpose is to propose the application models from our methodological contributions to diagnose the performance of rail dry port systems. First, we present the results of some ensemble methods applied on a first data set, which is fully labeled. Second, we show how can our co-selection approaches improve the performance of learning algorithms over partially labeled data provided by SRT
Guillaumin, Matthieu. "Données multimodales pour l'analyse d'image". Phd thesis, Grenoble, 2010. http://www.theses.fr/2010GRENM048.
Texto completo da fonteThis dissertation delves into the use of textual metadata for image understanding. We seek to exploit this additional textual information as weak supervision to improve the learning of recognition models. There is a recent and growing interest for methods that exploit such data because they can potentially alleviate the need for manual annotation, which is a costly and time-consuming process. We focus on two types of visual data with associated textual information. First, we exploit news images that come with descriptive captions to address several face related tasks, including face verification, which is the task of deciding whether two images depict the same individual, and face naming, the problem of associating faces in a data set to their correct names. Second, we consider data consisting of images with user tags. We explore models for automatically predicting tags for new images, i. E. Image auto-annotation, which can also used for keyword-based image search. We also study a multimodal semi-supervised learning scenario for image categorisation. In this setting, the tags are assumed to be present in both labelled and unlabelled training data, while they are absent from the test data. Our work builds on the observation that most of these tasks can be solved if perfectly adequate similarity measures are used. We therefore introduce novel approaches that involve metric learning, nearest neighbour models and graph-based methods to learn, from the visual and textual data, task-specific similarities. For faces, our similarities focus on the identities of the individuals while, for images, they address more general semantic visual concepts. Experimentally, our approaches achieve state-of-the-art results on several standard and challenging data sets. On both types of data, we clearly show that learning using additional textual information improves the performance of visual recognition systems
Guillaumin, Matthieu. "Données multimodales pour l'analyse d'image". Phd thesis, Grenoble, 2010. http://tel.archives-ouvertes.fr/tel-00522278/en/.
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