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Artykuły w czasopismach na temat "Apprentissage à partir de données d'intéraction"
Haddad, Maroua, Philippe Leray i Nahla Ben Amor. "Apprentissage des réseaux possibilistes à partir de données". Revue d'intelligence artificielle 29, nr 2 (28.04.2015): 229–52. http://dx.doi.org/10.3166/ria.29.229-252.
Pełny tekst źródłaDubé, Raymonde, Gabriel Goyette, Monique Lebrun i Marie-Thérèse Vachon. "Image mentale et apprentissage de l’orthographe lexicale". Articles 17, nr 2 (16.11.2009): 191–205. http://dx.doi.org/10.7202/900695ar.
Pełny tekst źródłaKhalfallah, Fédia, i Khaled Mellouli. "Apprentissage de la structure d'un réseau bayésien à partir d'une base de données". Revue d'intelligence artificielle 18, nr 2 (1.04.2004): 195–228. http://dx.doi.org/10.3166/ria.18.195-228.
Pełny tekst źródłaLapointe, Jacques. "Deux aspects du concept de besoin en éducation". Revue des sciences de l'éducation 5, nr 1 (15.10.2009): 21–38. http://dx.doi.org/10.7202/900095ar.
Pełny tekst źródłaDuroisin, Natacha, i Nancy Goyette. "Le défi des enseignants belges francophones dans l’élaboration de leurs séquences d’enseignement-apprentissage : prise en compte des théories sur l’autodétermination et le bien-être au travail". Phronesis 7, nr 4 (19.02.2019): 91–105. http://dx.doi.org/10.7202/1056322ar.
Pełny tekst źródłaMesny, Anne, i Jean-Sébastien Marcoux. "La recherche en gestion et les comités d’éthique : l’épreuve de la pratique1". Cahiers de recherche sociologique, nr 48 (19.05.2010): 111–27. http://dx.doi.org/10.7202/039768ar.
Pełny tekst źródłaBeaumier, France, i Ghyslain Parent. "Utilisation des stratégies d’apprentissage pour développer, par l’expérimentation, un sentiment d’efficacité personnelle chez les futurs enseignants". Études, nr 18-19 (9.07.2012): 133–48. http://dx.doi.org/10.7202/1010303ar.
Pełny tekst źródłaBESNIER, Jean-Baptiste, Frédéric CHERQUI, Gilles CHUZEVILLE i Aurélie LAPLANCHE. "Amélioration de la connaissance patrimoniale des réseaux d’assainissement de la métropole de Lyon". TSM 12 2023, TSM 12 2023 (20.12.2023): 169–77. http://dx.doi.org/10.36904/tsm/202312169.
Pełny tekst źródłaMinguzzi, Antonio, i Renato Passaro. "Apprentissage et culture d'entreprise dans les PME : une analyse explorative intersectorielle". Revue internationale P.M.E. 10, nr 2 (16.02.2012): 45–79. http://dx.doi.org/10.7202/1009023ar.
Pełny tekst źródłaGenet-Volet, Yvette, i Pauline Desrosiers. "Programmes d’apprentissage sollicitant des actions de coopération-opposition et transposition didactique". STAPS 16, nr 36 (1995): 29–44. http://dx.doi.org/10.3406/staps.1995.1008.
Pełny tekst źródłaRozprawy doktorskie na temat "Apprentissage à partir de données d'intéraction"
Sakhi, Otmane. "Offline Contextual Bandit : Theory and Large Scale Applications". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG011.
Pełny tekst źródłaThis thesis presents contributions to the problem of learning from logged interactions using the offline contextual bandit framework. We are interested in two related topics: (1) offline policy learning with performance certificates, and (2) fast and efficient policy learning applied to large scale, real world recommendation. For (1), we first leverage results from the distributionally robust optimisation framework to construct asymptotic, variance-sensitive bounds to evaluate policies' performances. These bounds lead to new, more practical learning objectives thanks to their composite nature and straightforward calibration. We then analyse the problem from the PAC-Bayesian perspective, and provide tighter, non-asymptotic bounds on the performance of policies. Our results motivate new strategies, that offer performance certificates before deploying the policies online. The newly derived strategies rely on composite learning objectives that do not require additional tuning. For (2), we first propose a hierarchical Bayesian model, that combines different signals, to efficiently estimate the quality of recommendation. We provide proper computational tools to scale the inference to real world problems, and demonstrate empirically the benefits of the approach in multiple scenarios. We then address the question of accelerating common policy optimisation approaches, particularly focusing on recommendation problems with catalogues of millions of items. We derive optimisation routines, based on new gradient approximations, computed in logarithmic time with respect to the catalogue size. Our approach improves on common, linear time gradient computations, yielding fast optimisation with no loss on the quality of the learned policies
Ferrandiz, Sylvain. "Apprentissage supervisé à partir de données séquentielles". Caen, 2006. http://www.theses.fr/2006CAEN2030.
Pełny tekst źródłaIn the data mining process, the main part of the data preparation step is devoted to feature construction and selection. The filter approach usually adopted requires evaluation methods for any kind of feature. We address the problem of the supervised evaluation of a sequential feature. We show that this problem is solved if a more general problem is tackled : that of the supervised evaluation of a similarity measure. We provide such an evaluation method. We first turn the problem into the search of a discriminating Voronoi partition. Then, we define a new supervised criterion evaluating such partitions and design a new optimised algorithm. The criterion automatically prevents from overfitting the data and the algorithm quickly provides a good solution. In the end, the method can be interpreted as a robust non parametric method for estimating the conditional density of a nominal target feature given a similarity measure defined from a descriptive feature. The method is experimented on many datasets. It is useful for answering questions like : which day of the week or which hourly time segment is the most relevant to discriminate customers from their call detailed records ? Which series allows to better estimate the customer need for a new service ?
Chevaleyre, Yann. "Apprentissage de règles à partir de données multi-instances". Paris 6, 2001. http://www.theses.fr/2001PA066502.
Pełny tekst źródłaDubois, Vincent. "Apprentissage approximatif et extraction de connaissances à partir de données textuelles". Nantes, 2003. http://www.theses.fr/2003NANT2001.
Pełny tekst źródłaJouve, Pierre-Emmanuel. "Apprentissage non supervisé et extraction de connaissances à partir de données". Lyon 2, 2003. http://theses.univ-lyon2.fr/documents/lyon2/2003/jouve_pe.
Pełny tekst źródłaGuillouet, Brendan. "Apprentissage statistique : application au trafic routier à partir de données structurées et aux données massives". Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30205/document.
Pełny tekst źródłaThis thesis focuses on machine learning techniques for application to big data. We first consider trajectories defined as sequences of geolocalized data. A hierarchical clustering is then applied on a new distance between trajectories (Symmetrized Segment-Path Distance) producing groups of trajectories which are then modeled with Gaussian mixture in order to describe individual movements. This modeling can be used in a generic way in order to resolve the following problems for road traffic : final destination, trip time or next location predictions. These examples show that our model can be applied to different traffic environments and that, once learned, can be applied to trajectories whose spatial and temporal characteristics are different. We also produce comparisons between different technologies which enable the application of machine learning methods on massive volumes of data
Elati, Mohamed. "Apprentissage de réseaux de régulation génétique à partir de données d'expression". Paris 13, 2007. http://www.theses.fr/2007PA132031.
Pełny tekst źródłaPradel, Bruno. "Evaluation des systèmes de recommandation à partir d'historiques de données". Paris 6, 2013. http://www.theses.fr/2013PA066263.
Pełny tekst źródłaThis thesis presents various experimental protocols leading to abetter offline estimation of errors in recommender systems. As a first contribution, results form a case study of a recommendersystem based on purchased data will be presented. Recommending itemsis a complex task that has been mainly studied considering solelyratings data. In this study, we put the stress on predicting thepurchase a customer will make rather than the rating he will assign toan item. While ratings data are not available for many industries andpurchases data widely used, very few studies considered purchasesdata. In that setting, we compare the performances of variouscollaborative filtering models from the litterature. We notably showthat some changes the training and testing phases, and theintroduction of contextual information lead to major changes of therelative perfomances of algorithms. The following contributions will focus on the study of ratings data. Asecond contribution will present our participation to the Challenge onContext-Aware Movie Recommendation. This challenge provides two majorchanges in the standard ratings prediction protocol: models areevaluated conisdering ratings metrics and tested on two specificsperiod of the year: Christmas and Oscars. We provides personnalizedrecommendation modeling the short-term evolution of the popularitiesof movies. Finally, we study the impact of the observation process of ratings onranking evaluation metrics. Users choose the items they want to rateand, as a result, ratings on items are not observed at random. First,some items receive a lot more ratings than others and secondly, highratings are more likely to be oberved than poor ones because usersmainly rate the items they likes. We propose a formal analysis ofthese effects on evaluation metrics and experiments on the Yahoo!Musicdataset, gathering standard and randomly collected ratings. We showthat considering missing ratings as negative during training phaseleads to good performances on the TopK task, but these performancescan be misleading favoring methods modeling the popularities of itemsmore than the real tastes of users
Liquière, Michel. "Apprentissage à partir d'objets structurés : conception et réalisation". Montpellier 2, 1990. http://www.theses.fr/1990MON20038.
Pełny tekst źródłaKhiali, Lynda. "Fouille de données à partir de séries temporelles d’images satellites". Thesis, Montpellier, 2018. http://www.theses.fr/2018MONTS046/document.
Pełny tekst źródłaNowadays, remotely sensed images constitute a rich source of information that can be leveraged to support several applications including risk prevention, land use planning, land cover classification and many other several tasks. In this thesis, Satellite Image Time Series (SITS) are analysed to depict the dynamic of natural and semi-natural habitats. The objective is to identify, organize and highlight the evolution patterns of these areas.We introduce an object-oriented method to analyse SITS that consider segmented satellites images. Firstly, we identify the evolution profiles of the objects in the time series. Then, we analyse these profiles using machine learning methods. To identify the evolution profiles, we explore all the objects to select a subset of objects (spatio-temporal entities/reference objects) to be tracked. The evolution of the selected spatio-temporal entities is described using evolution graphs.To analyse these evolution graphs, we introduced three contributions. The first contribution explores annual SITS. It analyses the evolution graphs using clustering algorithms, to identify similar evolutions among the spatio-temporal entities. In the second contribution, we perform a multi-annual cross-site analysis. We consider several study areas described by multi-annual SITS. We use the clustering algorithms to identify intra and inter-site similarities. In the third contribution, we introduce à semi-supervised method based on constrained clustering. We propose a method to select the constraints that will be used to guide the clustering and adapt the results to the user needs.Our contributions were evaluated on several study areas. The experimental results allow to pinpoint relevant landscape evolutions in each study sites. We also identify the common evolutions among the different sites. In addition, the constraint selection method proposed in the constrained clustering allows to identify relevant entities. Thus, the results obtained using the unsupervised learning were improved and adapted to meet the user needs
Części książek na temat "Apprentissage à partir de données d'intéraction"
PERKO, Gregor, i Patrice Pognan. "Dictionnaire langue maternelle - langue étrangère". W Dictionnaires et apprentissage des langues, 15–24. Editions des archives contemporaines, 2021. http://dx.doi.org/10.17184/eac.4499.
Pełny tekst źródłaJACQUEMONT, Mikaël, Thomas VUILLAUME, Alexandre BENOIT, Gilles MAURIN i Patrick LAMBERT. "Analyse d’images Cherenkov monotélescope par apprentissage profond". W Inversion et assimilation de données de télédétection, 303–35. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9142.ch9.
Pełny tekst źródłaATTO, Abdourrahmane M., Héla HADHRI, Flavien VERNIER i Emmanuel TROUVÉ. "Apprentissage multiclasse multi-étiquette de changements d’état à partir de séries chronologiques d’images". W Détection de changements et analyse des séries temporelles d’images 2, 247–71. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9057.ch6.
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