Academic literature on the topic 'Dissimilarités'
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Journal articles on the topic "Dissimilarités":
RAYNAUT, William, Chantal SOULE-DUPUY, and Nathalie VALLES-PARLANGEAU. "Dissimilarités entre jeux de données." Ingénierie des systèmes d'information 22, no. 3 (March 28, 2017): 35–63. http://dx.doi.org/10.3166/isi.22.3.35-63.
Bertrand, Patrice. "Classifications en classes recouvrantes ou non, et leurs dissimilarités." Mathématiques et sciences humaines, no. 190 (March 10, 2010): 59–87. http://dx.doi.org/10.4000/msh.11737.
Mahamadou, Zoubeyda. "Asymétries entre partenaires et confiance : le cas des alliances stratégiques entre PME et multinationales." Revue internationale P.M.E. 30, no. 1 (May 3, 2017): 57–84. http://dx.doi.org/10.7202/1039786ar.
Soumaila, Mounkaila, Rabiou Habou, Morou Boube, Djima Idrissou Tahirou (IM), Mahamane Ali, and Saadou Mahamane. "Comparative Floristic Analysis of the Classified Forest of Gorou Bassounga (Gaya) and the Total Wildlife Reserve of Tamou and their Conservation Values." Scholars Academic Journal of Biosciences 11, no. 10 (October 7, 2023): 338–45. http://dx.doi.org/10.36347/sajb.2023.v11i10.003.
Pugh, Brittany E., and Richard Field. "Effect of Canal Bank Engineering Disturbance on Plant Communities: Analysis of Taxonomic and Functional Beta Diversity." Land 12, no. 5 (May 18, 2023): 1090. http://dx.doi.org/10.3390/land12051090.
Wang, Liwei, Masashi Sugiyama, Cheng Yang, Kohei Hatano, and Jufu Feng. "Theory and Algorithm for Learning with Dissimilarity Functions." Neural Computation 21, no. 5 (May 2009): 1459–84. http://dx.doi.org/10.1162/neco.2008.08-06-805.
Torres-Manzanera, Emilio, Pavol Král, Vladimír Janiš, and Susana Montes. "Uncertainty-Aware Dissimilarity Measures for Interval-Valued Fuzzy Sets." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. 05 (September 30, 2020): 757–68. http://dx.doi.org/10.1142/s0218488520500324.
Rietveld, P., and H. Ouwersloot. "Intraregional Income Distribution and Poverty: Some Investigations for the Netherlands, 1960–81." Environment and Planning A: Economy and Space 21, no. 7 (July 1989): 881–904. http://dx.doi.org/10.1068/a210881.
Crossetti, Luciane Oliveira, Fabiana Schneck, Lacina Maria Freitas-Teixeira, and David da Motta-Marques. "The influence of environmental variables on spatial and temporal phytoplankton dissimilarity in a large shallow subtropical lake (Lake Mangueira, southern Brazil)." Acta Limnologica Brasiliensia 26, no. 2 (June 2014): 111–18. http://dx.doi.org/10.1590/s2179-975x2014000200002.
Plantinga, Anna M., Jun Chen, Robert R. Jenq, and Michael C. Wu. "pldist: ecological dissimilarities for paired and longitudinal microbiome association analysis." Bioinformatics 35, no. 19 (February 19, 2019): 3567–75. http://dx.doi.org/10.1093/bioinformatics/btz120.
Dissertations / Theses on the topic "Dissimilarités":
Seston, Morgan. "Dissimilarités de Robinson : algorithmes de reconnaissance et d'approximation." Aix-Marseille 2, 2008. http://theses.univ-amu.fr.lama.univ-amu.fr/2008AIX22045.pdf.
Baudrier, Etienne. "Comparaison d'images binaires reposant sur une mesure locale des dissimilarités : Application à la classification." Reims, 2005. http://theses.univ-reims.fr/exl-doc/GED00000305.pdf.
This PhD deals with the comparison of binary images that are not composed of a single pattern. The proposed method is then extended to gray level images. Using a measure example - the Hausdorff distance (HD) - a local measure is defined throught a window, and its properties depending on the window size and the global HD measure are proved. Thanks to them, a window-size criterion is defined so as the window to be adjusted to the local dissimilarity. The local dissimilarity map (LDM) is then defined when the window slides over all the compared images. The LDM can be defined with other measure than the HD using the same algorithm, nevertheless, the DH properties leads to a LDM fast computation. The LDM can be used as image dissimilarity visualization method or a tool to decide on image similarity. For this last point, a first step is a binary-image adapted multiresolution analysis which is base on the median morphological filter. This allows to have an resolution adapted to the researched similarity degree. A second step consists in using LDM information concerning the dissimilarities and their spatial distribution to compare the images. Several comparison methods are tested, the most efficient one is based on the SVM with the whole LDM as input data. The method efficiency is successfully tested on an ancient-impressions database and on a face database
Dalleau, Kevin. "Une approche stochastique à base d’arbres aléatoires pour le calcul de dissimilarités : application au clustering pour diverses structures de données." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0181.
The notion of distance, and more generally of dissimilarity, is an important one in data mining, especially in unsupervised approaches. The algorithms belonging to this class of methods aim at grouping objects in an homogeneous way, and many of them rely on a notion of dissimilarity, in order to quantify the proximity between objects. The choice of algorithms as well as that of dissimilarities is not trivial. Several elements can motivate these choices, such as the type of data – homogeneous data or not –, their representation – feature vectors, graphs –, or some of their characteristics – highly correlated, noisy, etc. –. Although many measures exist, their choice can become complex in some specific settings. This leads to additional complexity in data mining tasks. In this thesis, we present a new approach for computing dissimilarities based on random trees. It is an original approach, which has several advantages such as a great versatility. Indeed, using different dissimilarity calculation modules that we can plug to the method, it becomes possible to apply it in various settings. In particular, we present in this document two modules, enabling the computation of dissimilarities - and, in fine, clustering - on data structured as feature vectors, and on data in the form of graphs. We discuss the very promising results obtained by this approach, as well as the numerous perspectives that it opens, such as the computation of dissimilarity in the framework of attributed graphs, through a unified approach
Abou, Latif Firas. "Identification du profil des utilisateurs d’un hypermédia encyclopédique à l’aide de classifieurs basés sur des dissimilarités : création d’un composant d’un système expert pour Hypergéo." Thesis, Rouen, INSA, 2011. http://www.theses.fr/2011ISAM0004/document.
This thesis is devoted to identify the profile of hypermedia user, then to adapt it according to user’s profile. This profile is found by using supervised learning algorithm like SVM. The user model is one of the essential components of adaptive hypermedia. One way to characterize this model is to associate a user to a profile. Web Usage Mining (WUM) identifies this profile from traces. However, these techniques usually operate on large mass of data. In the case when not enough data are available, we propose to use the structure and the content of the hypermedia. Hence, we used supervised kernel learning algorithms for which we have defined the measure of similarity between traces based on a “distance” between documents of the site. Our approach was validated using synthetic data and then using real data from the traces of Hypergéo users, Hypergéo is an encyclopedic website specialized in geography. Our results were compared with those obtained using a techniques of WUM(the algorithm of characteristic patterns). Finally, our proposals to identify the profiles a posteriori led usto highlight five profiles. Hypergéo users are classified according to their interests when the “semantic distance” between documents is applied
Abou, Latif Firas. "Identification du profil des utilisateurs d'un hypermédia encyclopédique à l'aide de classifieurs basés sur des dissimilarités : création d'un composant d'un système expert pour Hypergéo." Phd thesis, INSA de Rouen, 2011. http://tel.archives-ouvertes.fr/tel-00625439.
Ketata, Ines. "Extraction et Modélisation de la cinétique du traceur en imagerie TEP pour la caractérisation des tissus tumoraux." Thesis, Reims, 2013. http://www.theses.fr/2013REIMS031/document.
The research of this thesis proposes in the context of the breast cancer characterization in order to achieve a new approach for the extraction and modeling of the tracer kinetics in PET imaging.The measurement of the counting rate of a tracer in a region of interest (ROI) estimated using an extension of a Real Valued Local Dissimilarity Map (RVLDM) proposed grayscale and the use of dynamic models as the method of factor analysis of medical image sequences (FAMIS) applied on the ROI enable an automatic early quantification of glucose metabolism. More specifically, it is to determine a new KFPQ empirical parameter. It is calculated from the two compartments obtained in the region of interest and tumor as assessed during the first pass of the 18F-FDG tracer in the early PET images
Cao, Hongliu. "Forêt aléatoire pour l'apprentissage multi-vues basé sur la dissimilarité : Application à la Radiomique." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR073/document.
The work of this thesis was initiated by a Radiomic learning problem. Radiomics is a medical discipline that aims at the large-scale analysis of data from traditional medical imaging to assist in the diagnosis and treatment of cancer. The main hypothesis of this discipline is that by extracting a large amount of information from the images, we can characterize the specificities of this pathology in a much better way than the human eye. To achieve this, Radiomics data are generally based on several types of images and/or several types of features (from images, clinical, genomic). This thesis approaches this problem from the perspective of Machine Learning (ML) and aims to propose a generic solution, adapted to any similar learning problem. To do this, we identify two types of ML problems behind Radiomics: (i) learning from high dimension, low sample size (HDLSS) and (ii) multiview learning. The solutions proposed in this manuscript exploit dissimilarity representations obtained using the Random Forest method. The use of dissimilarity representations makes it possible to overcome the well-known difficulties of learning high dimensional data, and to facilitate the joint analysis of the multiple descriptions, i.e. the views.The contributions of this thesis focus on the use of the dissimilarity easurement embedded in the Random Forest method for HDLSS multi-view learning. In particular, we present three main results: (i) the demonstration and analysis of the effectiveness of this measure for HDLSS multi-view learning; (ii) a new method for measuring dissimilarities from Random Forests, better adapted to this type of learning problem; and (iii) a new way to exploit the heterogeneity of views, using a dynamic combination mechanism. These results have been obtained on radiomic data but also on classical multi-view learning problems
Drouet, d'Aubigny Gérard Romier Guy Van Cutsem Bernard. "L'analyse multidimensionnelle des données de dissimilarité." S.l. : Université Grenoble 1, 2008. http://tel.archives-ouvertes.fr/tel-00332393.
Zhu, Xibin [Verfasser]. "Adaptive prototype-based dissimilarity learning / Xibin Zhu." Bielefeld : Universitätsbibliothek Bielefeld, 2015. http://d-nb.info/1072224704/34.
Ortiz-Reynoso, Alejandra. "Perceiving similarity and dissimilarity in diverse teams." Thesis, University of Sheffield, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422641.
Books on the topic "Dissimilarités":
Van Cutsem, Bernard, ed. Classification and Dissimilarity Analysis. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2686-4.
1932-, Cutsem Bernard van, ed. Classification and dissimilarity analysis. New York: Springer-Verlag, 1994.
India) Tao Art Gallery (Mumbai. East, West, North, South, Centre: Similarities & dissimilarities. Mumbai: Tao Art Gallery, 2001.
Shklovskiǐ, Viktor Borisovich. Bowstring: On the dissimilarity of the similar. Champaign: Dalkey Archive Press, 2011.
Günes, M., D. G. Reina, J. M. Garcia Campos, and S. L. Toral. Mobile Ad Hoc Network Protocols Based on Dissimilarity Metrics. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62740-3.
Pękalska, Elżbieta. The dissimilarity representation for pattern recognition: Foundations and applications. Singapore: World Scientific, 2005.
Padmore, Joanne. An information based measure of dissimilarity for hierarchical cluster analysis. Sheffield: Sheffield University Management School, 1993.
United States. Army Aviation Systems Command. and Ames Research Center, eds. Effects of blade-to-blade dissimilarities on rotor-body lead-lag dynamics. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1986.
United States. Army Aviation Systems Command. and Ames Research Center, eds. Effects of blade-to-blade dissimilarities on rotor-body lead-lag dynamics. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1986.
United States. Army Aviation Systems Command. and Ames Research Center, eds. Effects of blade-to-blade dissimilarities on rotor-body lead-lag dynamics. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1986.
Book chapters on the topic "Dissimilarités":
Van Cutsem, Bernard. "Introduction." In Classification and Dissimilarity Analysis, 1–4. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2686-4_1.
Critchley, Frank, and Bernard Fichet. "The partial order by inclusion of the principal classes of dissimilarity on a finite set, and some of their basic properties." In Classification and Dissimilarity Analysis, 5–65. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2686-4_2.
Joly, Serge, and Georges Le Calvé. "Similarity functions." In Classification and Dissimilarity Analysis, 67–86. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2686-4_3.
Critchley, Frank, and Bernard Van Cutsem. "An order-theoretic unification and generalisation of certain fundamental bijections in mathematical classification. I." In Classification and Dissimilarity Analysis, 87–119. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2686-4_4.
Critchley, Frank, and Bernard Van Cutsem. "An order-theoretic unification and generalisation of certain fundamental bijections in mathematical classification. II." In Classification and Dissimilarity Analysis, 121–47. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2686-4_5.
Leclerc, Bruno. "The residuation model for the ordinal construction of dissimilarities and other valued objects." In Classification and Dissimilarity Analysis, 149–72. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2686-4_6.
Critchley, Frank. "On exchangeability-based equivalence relations induced by strongly Robinson and, in particular, by quadripolar Robinson dissimilarity matrices." In Classification and Dissimilarity Analysis, 173–99. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2686-4_7.
Fichet, Bernard. "Dimensionality problems in L 1-norm representations." In Classification and Dissimilarity Analysis, 201–24. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2686-4_8.
López-Oriona, Ángel, José A. Vilar, and Pierpaolo D’Urso. "Unsupervised Classification of Categorical Time Series Through Innovative Distances." In Studies in Classification, Data Analysis, and Knowledge Organization, 233–41. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_26.
Güneş, M., D. G. Reina, J. M. Garcia Campos, and S. L. Toral. "Dissimilarity Metrics." In SpringerBriefs in Electrical and Computer Engineering, 25–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62740-3_4.
Conference papers on the topic "Dissimilarités":
Valls Dalmau, Francesc, and Josep Roca Cladera. "Quantification of similarity between land cover categories." In International Conference Virtual City and Territory. Rio de Janeiro: Universidade Federal do Rio de Janeiro, 2012. http://dx.doi.org/10.5821/ctv.7883.
Aryal, Sunil, Kai Ming Ting, Gholamreza Haffari, and Takashi Washio. "Mp-Dissimilarity: A Data Dependent Dissimilarity Measure." In 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.33.
Dehghan, M. A., M. Keshavarzi, M. Mashinchi, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "3-Dissimilarities." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637798.
García-Escudero, Daniel, Berta Bardí Milà, Francisco Fayos Vallés, and Francesc Valls Dalmau. "Course clustering based on the phylogenic tree of students’ grades in architectural degree." In SEFI 50th Annual conference of The European Society for Engineering Education. Barcelona: Universitat Politècnica de Catalunya, 2022. http://dx.doi.org/10.5821/conference-9788412322262.1191.
Chitturi, Bhadrachalam, and K. S. Krishnaveni. "Expected genomic dissimilarity." In 2019 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO). IEEE, 2019. http://dx.doi.org/10.1109/icmsao.2019.8880405.
Xu, Xiao, Qing Zhao, and Ananthram Swami. "Learning from Dissimilarity." In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645546.
Ouyang, Hua, and Alex Gray. "Learning dissimilarities by ranking." In the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390248.
Bicego, Manuele. "Dissimilarity Random Forest Clustering." In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00105.
Cooper, J., S. Venkatesh, and L. Kitchen. "The dissimilarity corner detector." In Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments. IEEE, 1991. http://dx.doi.org/10.1109/icar.1991.240450.
Zhang, Weifeng, and Zengchang Qin. "Dissimilarity measure of logical expressions." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5581066.
Reports on the topic "Dissimilarités":
Oh, Man-Suk, and Adrian Raftery. Model-based Clustering with Dissimilarities: A Bayesian Approach. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada459759.
Bubacz, Jacob A., Hana T. Chmielewski, Alexander E. Pape, Andrew J. Depersio, Lee M. Hively, Robert K. Abercrombie, and Shane Boone. Phase Space Dissimilarity Measures for Structural Health Monitoring. Office of Scientific and Technical Information (OSTI), November 2011. http://dx.doi.org/10.2172/1029952.
Jones, Robert, Molly Creagar, Michael Musty, Randall Reynolds, Scott Slone, and Robyn Barbato. A 𝘬-means analysis of the voltage response of a soil-based microbial fuel cell to an injected military-relevant compound (urea). Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45940.
Dahlstedt, Inge, and Henrik Emilsson. Growing apart : Increasing labour market segmentation of EU-13 workers in Sweden. Malmö Institute for Studies of Migration, Diversity and Welfare (MIM), Malmö University, 2023. http://dx.doi.org/10.24834/isbn.9789178774395.
Wittberg, Sara. Standardisering för individuell prövning: En kartläggning av kommunala riktlinjer för bistånd till äldreomsorg – funktion och inverkan. Linköping University Electronic Press, August 2023. http://dx.doi.org/10.3384/9789180752886.
Svahn, Emma. Faktablad – Resultat från övervakningen av kustfisk – Kvädöfjärden (Egentliga Östersjön) 1989‒2022. Institutionen för akvatiska resurser, Sveriges lantbruksuniversitet, 2024. http://dx.doi.org/10.54612/a.4e98k7nsrq.