Academic literature on the topic 'Apprentissage de similarités'
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Journal articles on the topic "Apprentissage de similarités":
Rogers, W., Liying Cheng, and Huiqin Hu. "ESL/EFL Instructors’ Beliefs about Assessment and Evaluation." Comparative and International Education 36, no. 1 (June 1, 2007). http://dx.doi.org/10.5206/cie-eci.v36i1.9088.
Mathany, Clarke, Katie M. Clow, and Erin D. Aspenlieder. "Exploring the Role of the Scholarship of Teaching and Learning in the Context of the Professional Identities of Faculty, Graduate Students, and Staff in Higher Education." Canadian Journal for the Scholarship of Teaching and Learning 8, no. 3 (December 4, 2017). http://dx.doi.org/10.5206/cjsotl-rcacea.2017.3.10.
Dissertations / Theses on the topic "Apprentissage de similarités":
Boyer, Laurent. "Apprentissage probabiliste de similarités d'édition." Phd thesis, Université Jean Monnet - Saint-Etienne, 2011. http://tel.archives-ouvertes.fr/tel-00718835.
Brezellec, Pierre. "Techniques d'apprentissage par explication et détections de similarités." Paris 13, 1992. http://www.theses.fr/1992PA132033.
Philippeau, Jérémy. "Apprentissage de similarités pour l'aide à l'organisation de contenus audiovisuels." Toulouse 3, 2009. http://thesesups.ups-tlse.fr/564/.
In the perspective of new usages in the field of the access to audiovisual archives, we have created a semi-automatic system that helps a user to organize audiovisual contents while performing tasks of classification, characterization, identification and ranking. To do so, we propose to use a new vocabulary, different from the one already available in INA documentary notices, to answer needs which can not be easily defined with words. We have conceived a graphical interface based on graph formalism designed to express an organisational task. The digital similarity is a good tool in respect with the handled elements which are informational objects shown on the computer screen and the automatically extracted audio and video low-level features. We have made the choice to estimate the similarity between those elements with a predictive process through a statistical model. Among the numerous existing models, the statistical prediction based on the univaried regression and on support vectors has been chosen. H)
Grimal, Clément. "Apprentissage de co-similarités pour la classification automatique de données monovues et multivues." Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENM092/document.
Machine learning consists in conceiving computer programs capable of learning from their environment, or from data. Different kind of learning exist, depending on what the program is learning, or in which context it learns, which naturally forms different tasks. Similarity measures play a predominant role in most of these tasks, which is the reason why this thesis focus on their study. More specifically, we are focusing on data clustering, a so called non supervised learning task, in which the goal of the program is to organize a set of objects into several clusters, in such a way that similar objects are grouped together. In many applications, these objects (documents for instance) are described by their links to other types of objects (words for instance), that can be clustered as well. This case is referred to as co-clustering, and in this thesis we study and improve the co-similarity algorithm XSim. We demonstrate that these improvements enable the algorithm to outperform the state of the art methods. Additionally, it is frequent that these objects are linked to more than one other type of objects, the data that describe these multiple relations between these various types of objects are called multiview. Classical methods are generally not able to consider and use all the information contained in these data. For this reason, we present in this thesis a new multiview similarity algorithm called MVSim, that can be considered as a multiview extension of the XSim algorithm. We demonstrate that this method outperforms state of the art multiview methods, as well as classical approaches, thus validating the interest of the multiview aspect. Finally, we also describe how to use the MVSim algorithm to cluster large-scale single-view data, by first splitting it in multiple subsets. We demonstrate that this approach allows to significantly reduce the running time and the memory footprint of the method, while slightly lowering the quality of the obtained clustering compared to a straightforward approach with no splitting
Champesme, Marc. "Apprentissage par détection de similarités utilisant le formalisme des graphes conceptuels." Paris 13, 1993. http://www.theses.fr/1993PA132004.
Grimal, Clement. "Apprentissage de co-similarités pour la classification automatique de données monovues et multivues." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00819840.
Akgül, Ceyhun Burak. "Descripteurs de forme basés sur la densité probabiliste et apprentissage des similarités pour la recherche d'objets 3D." Phd thesis, Télécom ParisTech, 2007. http://pastel.archives-ouvertes.fr/pastel-00003154.
Akgül, Ceyhun Burak. "Descripteurs de forme basés sur la densité de probabilité et apprentissage des similarités pour la recherche d'objets 3D." Paris, ENST, 2007. http://www.theses.fr/2007ENST0026.
Content-based retrieval research aims at developing search engines that would allow users to perform a query by similarity of content. This thesis deals with two fundamentals problems in content-based 3D object retrieval : (1) How to describe a 3D shape to obtain a reliable representative for the subsequent task of similarity search? (2) How to supervise the search process to learn inter-shape similarities for more effective and semantic retrieval? Concerning the first problem, we develop a novel 3D shape description scheme based on probability density of multivariate local surface features. We constructively obtain local characterizations of 3D points and then summarize the resulting local shape information into a global shape descriptor. For probability density estimation, we use the general purpose kernel density estimation methodology, coupled with a fast approximation algorithm: the fast Gauss transform. Experiments that we have conducted on several 3D object databases show that density-based descriptors are very fast to compute and very effective for 3D similarity search. Concerning the second problem, we propose a similarity learning scheme. Our approach relies on combining multiple similarity scores by optimizing a convex regularized version of the empirical ranking risk criterion. This score fusion approach to similarity learning is applicable to a variety of search engine problems. In this work, we demonstrate its effectiveness in 3D object retrieval
Morvant, Emilie. "Apprentissage de vote de majorité pour la classification supervisée et l'adaptation de domaine : approches PAC-Bayésiennes et combinaison de similarités." Phd thesis, Aix-Marseille Université, 2013. http://tel.archives-ouvertes.fr/tel-00879072.
Le, Boudic-Jamin Mathilde. "Similarités et divergences, globales et locales, entre structures protéiques." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S119/document.
This thesis focusses on local and global similarities and divergences inside protein structures. First, structures are scored, with criteria of similarity and distance in order to provide a supervised classification. This structural domain classification inside existing hierarchical databases is possible by using dominances and learning. These methods allow to assign new domains with accuracy and exactly. Second we focusses on local similarities and proposed a method of protein comparison modelisation inside graphs. Graph traversal allows to find protein similar substructures. This method is based on compatibility between elements and criterion of distances. We can use it and detect events such that circular permutations, hinges and structural motif repeats. Finally we propose a new approach of accurate protein structure analysis that focused on divergences between similar structures
Books on the topic "Apprentissage de similarités":
Similarity and analogical reasoning. Cambridge: Cambridge University Press, 1989.