Academic literature on the topic 'Similarité possibiliste'
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Journal articles on the topic "Similarité possibiliste"
Goulding, Brandon W., and Ori Friedman. "A Similarity Heuristic in Children’s Possibility Judgments." Child Development 92, no. 2 (January 31, 2021): 662–71. http://dx.doi.org/10.1111/cdev.13534.
Full textJuarez, Jose M., Francisco Guil, Jose Palma, and Roque Marin. "Temporal similarity by measuring possibilistic uncertainty in CBR." Fuzzy Sets and Systems 160, no. 2 (January 2009): 214–30. http://dx.doi.org/10.1016/j.fss.2008.05.017.
Full textBashir, Maruah, Abdul Razak Salleh, and Shawkat Alkhazaleh. "Possibility Intuitionistic Fuzzy Soft Set." Advances in Decision Sciences 2012 (March 13, 2012): 1–24. http://dx.doi.org/10.1155/2012/404325.
Full textYu yu Liao, Ke xin Jia, and Zi shu He. "Similarity Measure based Robust Possibilistic C-means Clustering Algorithms." Journal of Convergence Information Technology 6, no. 12 (December 31, 2011): 129–38. http://dx.doi.org/10.4156/jcit.vol6.issue12.17.
Full textAlkhazaleh, Shawkat, Abdul Razak Salleh, and Nasruddin Hassan. "Possibility Fuzzy Soft Set." Advances in Decision Sciences 2011 (September 8, 2011): 1–18. http://dx.doi.org/10.1155/2011/479756.
Full textCharfi, Amal, Sonda Ammar Bouhamed, Eloi Bosse, Imene Khanfir Kallel, Wassim Bouchaala, Basel Solaiman, and Nabil Derbel. "Possibilistic Similarity Measures for Data Science and Machine Learning Applications." IEEE Access 8 (2020): 49198–211. http://dx.doi.org/10.1109/access.2020.2979553.
Full textZadeh, Lotfi A. "A note on similarity-based definitions of possibility and probability." Information Sciences 267 (May 2014): 334–36. http://dx.doi.org/10.1016/j.ins.2014.01.046.
Full textSelvachandran, Ganeshsree, and Sunil Jacob John. "Possibility Interval-Valued Vague Soft Expert Sets and Its Similarity Measure." International Journal of Fuzzy System Applications 6, no. 1 (January 2017): 108–21. http://dx.doi.org/10.4018/ijfsa.2017010106.
Full textFishman, Alon. "English similarity predicates construe particular dimensions of similarity." Cognitive Linguistics 31, no. 3 (August 27, 2020): 453–84. http://dx.doi.org/10.1515/cog-2018-0086.
Full textBai, Xiangzhi, Yuxuan Zhang, Haonan Liu, and Zhiguo Chen. "Similarity Measure-Based Possibilistic FCM With Label Information for Brain MRI Segmentation." IEEE Transactions on Cybernetics 49, no. 7 (July 2019): 2618–30. http://dx.doi.org/10.1109/tcyb.2018.2830977.
Full textDissertations / Theses on the topic "Similarité possibiliste"
Ben, marzouka Wided. "Modélisation conjointe des connaissances humaines et machines pour de meilleurs approches d’aide à la décision." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0452.
Full textThis research introduces a novel approach to modeling human and machine knowledge in the fault identification (FI). It makes three key contributions:(1) the structuring of the Hypothetical Case Base (H-CB), which captures the sequential reasoning of experts and organizes observations to enhance FI; (2) knowledge extraction, incorporating possibilistic reasoning to manage the heterogeneity of observed primitives, introduce a Possibilistic Knowledge Vector (PSK), and improve diagnostic accuracy through possibilistic similarity; and (3) an incremental learning mechanism that enables the continuous integration of new knowledge, allowing the system to evolve with newly encountered cases and refine decision-making. The proposed approach, Possibilistic Hypothetical Case-Based Reasoning (PH-CBR), has been experimentally validated. Results show significant improvements over traditional case-based reasoning methods, reducing the number of primitives required for FI and enhancing industrial machine efficiency by minimizing diagnostic time
Jenhani, Ilyes. "From possibilistic similarity measures to possibilistic decision trees." Thesis, Artois, 2010. http://www.theses.fr/2010ARTO0402/document.
Full textThis thesis concerns two important issues in machine learning and reasoning under uncertainty: how to evaluate a similarity relation between two uncertain pieces of information, and how to perform classification from uncertain data. Our first main contribution is to propose a so-called possibilistic decision tree which allows to induce decision trees from training data afflicted with imperfection. More precisely, it handles training data characterized by uncertain class labels where uncertainty is modeled within the quantitative possibility theory framework. We have developed three possibilistic decision tree approaches. For each approach, we were faced and solved typical questions for inducing possibilistic decision trees such as how to define an attribute selection measure when classes are represented by possibility distributions, how to find the stopping criteria and how leaves should be labeled in such uncertain context. The first approach, so-called, non-specificity-based possibilistic decision tree uses the concept of non-specificity relative to possibility theory in its attribute selection measure component. This approach keeps up the possibility distributions within all the stages of the building procedure and especially when evaluating the informativeness of the attributes in the attribute selection step. Conversely, the second and the third approaches, so-called similarity-based possibilistic decision tree and clustering-based possibilistic decision tree, automatically, get rid of possibility distributions in their attribute selection measure. This strategy has allowed them to extend the gain ratio criterion and hence to extend the C4.5 algorithm to handle possibilistic labeled data. These two possibilistic decision tree approaches are mainly based on the concept of similarity between possibility distributions.This latter issue constitutes our second main contribution in this thesis. In fact, an important challenge was to provide a property-based analysis of possibilistic similarity measures. After showing the important role that inconsistency could play in assessing possibilistic similarity, a new inconsistency-based possibilistic similarity measure, so-called information affinity has been proposed. This measure satisfies a set of natural properties that we have established. Finally, we have conducted experiments to show the feasibility and to compare the different possibilistic decision tree approaches developed in this thesis
Kiourti, Ira Georgia. "Real impossible worlds : the bounds of possibility." Thesis, University of St Andrews, 2010. http://hdl.handle.net/10023/924.
Full textWu, Chun-Kuan, and 吳俊堃. "Possibility of urban flora stepping stones:a study of plant similarity between urban derelict lands and suburban natural areas in Taipei." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/98576784532663727173.
Full text中國文化大學
景觀學系碩士班
92
Abstract The concept of stepping-stones is applied in landscape planning and design. In this study, I am concerned if stepping-stones could maintain their function in highly-disturbed urban environments. I used the concept of “source and sink” to discuss the similarity of plants between the urban derelict lands in Taipei and its suburban natural area, and conducted a survey to analyze whether spatial distances affected plant similarity. A total of 60 sites were surveyed, with 30 urban derelict sites and 30 suburban natural vegetation sites. I analyzed the vegetation using ordination techniques and numerical methods derived from Mantel’s Test. The plants in urban derelict lands is mainly early successional. Plant diversity is higher in the urban edge than in urban cores. The spatial distance between two sample-plots can affect species similarity, but is not the main factor. The main factor is the successional stage. When successional stage is similar between two plots, their species similarity increases as their spatial distance decreases. We conclude that sites in urban edges closer to the suburbs have a higher possibility of serving as functional urban flora stepping stones than those in urban cores. Finally, the implications of the results are discussed in the context of urban green-space planning and design, in which I maintain that the right decision is very much a function of place: the preservation of biodiversity, though important, may not always be appropriate in the urban environment, because the urban matrix may be too harsh for biota, such that urban stepping stones may not function properly in urban cores. Landscape planning must be based on the characteristics of the environment.
AL, SUN Mohammad Homam. "Indexation guidée par les connaissances en imagerie médicale." Phd thesis, 2012. http://tel.archives-ouvertes.fr/tel-00719587.
Full textNohejl, Jiří. "Obraz jako příklad a vzor v kontextu jeho vlivu a rozšíření na prožívání života člověka." Master's thesis, 2012. http://www.nusl.cz/ntk/nusl-307518.
Full textBooks on the topic "Similarité possibiliste"
Stirr, Anna Marie. Conclusion. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190631970.003.0009.
Full textLópez, Marissa K. Racial Immanence. NYU Press, 2019. http://dx.doi.org/10.18574/nyu/9781479807727.001.0001.
Full textBook chapters on the topic "Similarité possibiliste"
Solaiman, Basel, and Éloi Bossé. "Possibilistic Similarity Measures." In Possibility Theory for the Design of Information Fusion Systems, 83–135. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32853-5_4.
Full textJenhani, Ilyes, Salem Benferhat, and Zied Elouedi. "Possibilistic Similarity Measures." In Foundations of Reasoning under Uncertainty, 99–123. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10728-3_6.
Full textDahabiah, Anas, John Puentes, and Basel Solaiman. "Possibilistic Similarity Estimation and Visualization." In Lecture Notes in Computer Science, 273–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04417-5_26.
Full textRoca-Royes, Sonia. "Similarity and Possibility: An Epistemology of de re Possibility for Concrete Entities." In Modal Epistemology After Rationalism, 221–45. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44309-6_12.
Full textTanaka, Hideo, and Peijun Guo. "Possibilistic Data Analysis and Its Similarity to Rough Sets." In Data Mining, Rough Sets and Granular Computing, 518–36. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1791-1_26.
Full textJenhani, Ilyes, Nahla Ben Amor, Zied Elouedi, Salem Benferhat, and Khaled Mellouli. "Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information." In Lecture Notes in Computer Science, 840–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75256-1_73.
Full textJia, Kexin, Miao He, and Ting Cheng. "A New Similarity Measure Based Robust Possibilistic C-Means Clustering Algorithm." In Web Information Systems and Mining, 335–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23982-3_41.
Full textSlokom, Manel, and Raouia Ayachi. "A Hybrid User and Item Based Collaborative Filtering Approach by Possibilistic Similarity Fusion." In Advances in Combining Intelligent Methods, 125–47. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46200-4_7.
Full textMazzucotelli, Elisabetta, Anna Maria Mastrangelo, Francesca Desiderio, Delfina Barabaschi, Marco Maccaferri, Roberto Tuberosa, and Luigi Cattivelli. "Gene Flow Between Tetraploid and Hexaploid Wheat for Breeding Innovation." In Compendium of Plant Genomes, 135–63. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-38294-9_8.
Full textEnembreck, Flávia Luisa Pires, Maria do Carmo Duarte Freitas, Luís Bragança, and Sergio Fernando Tavares. "Potential Synergy Between Agile Management and the Mindset of Circular Economy in Construction Projects." In Creating a Roadmap Towards Circularity in the Built Environment, 239–48. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45980-1_20.
Full textConference papers on the topic "Similarité possibiliste"
"Possibilistic Similarity based Image Classification." In International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004265002710275.
Full textZhang, Chunhui, Yiming Zhou, and Trevor Martin. "Similarity Based Fuzzy and Possibilistic c-means Algorithm." In 11th Joint Conference on Information Sciences. Paris, France: Atlantis Press, 2008. http://dx.doi.org/10.2991/jcis.2008.9.
Full textSkrjanc, Igor, Araceli Sanchis de Miguel, Jose Antonio Iglesias, Agapito Ledezma, and Dejan Dovzan. "Evolving Cauchy possibilistic clustering based on cosine similarity for monitoring cyber systems." In 2017 Evolving and Adaptive Intelligent Systems (EAIS). IEEE, 2017. http://dx.doi.org/10.1109/eais.2017.7954825.
Full text"A Method of Pixel Unmixing by Classes based on the Possibilistic Similarity." In International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004826202200226.
Full textRadhakrishna, Vangipuram, Puligadda Veereswara Kumar, and Vinjamuri Janaki. "Looking into the possibility of novel dissimilarity measure to discover similarity profiled temporal association patterns in IoT." In 2016 International Conference on Engineering & MIS (ICEMIS). IEEE, 2016. http://dx.doi.org/10.1109/icemis.2016.7745353.
Full textZhao, Rongrong, and Alexander J. Smits. "Preliminary Crossed Hot-Wire Measurements in High Reynolds Number Turbulent Pipe Flow." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-55650.
Full textSong, Ni, Wei Zhang, Sha Zhou, and Qian Wang. "Rational Solutions and Rogue Waves in Nonlinear Schrödinger Equation With Varying Coefficients." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34142.
Full textAmer, Nelly S., and Hesham A. Hefny. "Analytical formulas for similarity, possibility and distinguishability measures of Cauchy type fuzzy sets with comparison to Gaussian fuzzy sets." In 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, 2015. http://dx.doi.org/10.1109/intelcis.2015.7397257.
Full textFesyun, Andrey G. "What a Buddhist Will See at Hell (Diversity of Punishments and a Possibility to Avoid Them)." In ВОСТОК-ФОКУС: актуальные вопросы изучения истории, международ ных отношений и культур стран Востока: материалы VII Международной научно-практической конференции. IPC NSU, 2024. https://doi.org/10.25205/978-5-4437-1701-2-31.
Full textDu, Liu, Kyung K. Choi, and Ikjin Lee. "Robust Design Concept in Possibility Theory and Optimization for System With Both Random and Fuzzy Input Variables." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35106.
Full textReports on the topic "Similarité possibiliste"
Soloviev, Vladimir, Oleksandr Serdiuk, Serhiy Semerikov, and Arnold Kiv. Recurrence plot-based analysis of financial-economic crashes. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4121.
Full textTzfira, Tzvi, Michael Elbaum, and Sharon Wolf. DNA transfer by Agrobacterium: a cooperative interaction of ssDNA, virulence proteins, and plant host factors. United States Department of Agriculture, December 2005. http://dx.doi.org/10.32747/2005.7695881.bard.
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