Relevant bibliographies by topics / Possibilistic similarity

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Possibilistic similarity'

Author: Grafiati
Published: 8 February 2025

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Journal articles on the topic "Possibilistic similarity"

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SGARRO, ANDREA. "UTILITIES AND DISTORTIONS: AN OBJECTIVE APPROACH TO POSSIBILITIES CODING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 13, no. 02 (2005): 139–61. http://dx.doi.org/10.1142/s0218488505003369.

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We re-take the possibilistic (as opposed to probabilistic) approach to information coding put forward in 1,2. To enhance the possibilistic approach also outside the realm of "subjective" uncertainties, in this paper we adopt an "objective" interpretation of possibilistic source coding based on utility functions and an "objective" interpretation of possibilistic channel coding based on distortion measures and similarity indices. We stress the relationship between possibilistic coding as based on distortions between sequences and algebraic coding as based on minimum distances between codewords.
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Singh, Abhishek, and Anil Kumar. "Introduction of Local Spatial Constraints and Local Similarity Estimation in Possibilistic c-Means Algorithm for Remotely Sensed Imagery." Journal of Modeling and Optimization 11, no. 1 (2019): 51–56. http://dx.doi.org/10.32732/jmo.2019.11.1.51.

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This paper presents a unique Possibilistic c-Means with constraints (PCM-S) with Adaptive Possibilistic Local Information c-Means (ADPLICM) in a supervised way by incorporating local information through local spatial constraints and local similarity measures in Possibilistic c-Means Algorithm. PCM-S with ADPLICM overcome the limitations of the known Possibilistic c-Means (PCM) and Possibilistic c-Means with constraints (PCM-S) algorithms. The major contribution of proposed algorithm to ensure the noise resistance in the presence of random salt & pepper noise. The effectiveness of proposed
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Miyamoto, Sadaaki, Youhei Kuroda, and Kenta Arai. "Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (2008): 448–53. http://dx.doi.org/10.20965/jaciii.2008.p0448.

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In addition to fuzzy c-means, possibilistic clustering is useful because it is robust against noise in data. The generated clusters are, however, strongly dependent on an initial value. We propose a family of algorithms for sequentially generating clusters “one cluster at a time,” which includes possibilistic medoid clustering. These algorithms automatically determine the number of clusters. Due to possibilistic clustering's similarity to the mountain clustering by Yager and Filev, we compare their formulation and performance in numerical examples.
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Juarez, Jose M., Francisco Guil, Jose Palma, and Roque Marin. "Temporal similarity by measuring possibilistic uncertainty in CBR." Fuzzy Sets and Systems 160, no. 2 (2009): 214–30. http://dx.doi.org/10.1016/j.fss.2008.05.017.

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Yu 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 (2011): 129–38. http://dx.doi.org/10.4156/jcit.vol6.issue12.17.

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Charfi, Amal, Sonda Ammar Bouhamed, Eloi Bosse, et al. "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.

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Bai, 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 (2019): 2618–30. http://dx.doi.org/10.1109/tcyb.2018.2830977.

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Alsahwa, B., B. Solaiman, É. Bossé, S. Almouahed, and D. Guériot. "A Method of Spatial Unmixing Based on Possibilistic Similarity in Soft Pattern Classification." Fuzzy Information and Engineering 8, no. 3 (2016): 295–314. http://dx.doi.org/10.1016/j.fiae.2016.11.004.

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Devi, R. "Unsupervised Kernel-Induced Fuzzy Possibilistic C-Means Technique in Investigating Real-World Data." Journal of Physics: Conference Series 2199, no. 1 (2022): 012033. http://dx.doi.org/10.1088/1742-6596/2199/1/012033.

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Abstract The goal of this study is to break down a large dataset into meaningful groupings. Due to the vast dimension and significant resemblance seen among data, exploring divided clusters in real-world datasets is the most difficult assignment. As a result, this work proposes a fuzzy set-based unsupervised effective clustering technique that includes possibilistic memberships, and fuzzy membership degrees into the membership, weighted Cauchy kernel-based similarity measure and center equations. The empirical findings demonstrate the feasibility of the proposed effective clustering technique.
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Schockaert, Steven, and Henri Prade. "An Inconsistency-Tolerant Approach to Information Merging Based on Proposition Relaxation." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 363–68. http://dx.doi.org/10.1609/aaai.v24i1.7583.

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Inconsistencies between different information sources may arise because of statements that are inaccurate, albeit not completely false. In such scenarios, the most natural way to restore consistency is often to interpret assertions in a more flexible way, i.e. to enlarge (or relax) their meaning. As this process inherently requires extra-logical information about the meaning of atoms, extensions of classical merging operators are needed. In this paper, we introduce syntactic merging operators, based on possibilistic logic, which employ background knowledge about the similarity of atomic propos
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Dissertations / Theses on the topic "Possibilistic similarity"

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Jenhani, Ilyes. "From possibilistic similarity measures to possibilistic decision trees." Thesis, Artois, 2010. http://www.theses.fr/2010ARTO0402/document.

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Cette thèse traite deux problèmes importants dans les domaine de l'apprentissage automatique et du raisonnement dans l'incertain : comment évaluer une relation de similarité entre deux informations incertaines et comment assurer la classification \`a partir de données incertaines. Notre première principale contribution est de proposer une approche, appelée arbre de décision possibiliste, qui permet de construire des arbres de décision à partir de données d'apprentissage imparfaites. Plus précisément, elle traite des données caractérisées par des classes incertaines o\`u l'incertitude est modél
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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.

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Ce travail de recherche propose une nouvelle approche de modélisation des connaissances humaines et machines dans le processus d’identification des pannes (IP). Trois contributions majeures sont apportées : (1) la structuration de la base de cas hypothétiques (H-CB), qui reflète le raisonnement séquentiel des experts et permet une meilleure organisation des observations pour optimiser l’IP ; (2) l’extraction des connaissances, intégrant un raisonnement possibiliste pour traiter l’hétérogénéité des primitives observées, introduire un vecteur de connaissance possibiliste (PSK) et améliorer la pr
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Book chapters on the topic "Possibilistic similarity"

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Solaiman, Basel, and Éloi Bossé. "Possibilistic Similarity Measures." In Possibility Theory for the Design of Information Fusion Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32853-5_4.

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Jenhani, Ilyes, Salem Benferhat, and Zied Elouedi. "Possibilistic Similarity Measures." In Foundations of Reasoning under Uncertainty. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10728-3_6.

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Dahabiah, Anas, John Puentes, and Basel Solaiman. "Possibilistic Similarity Estimation and Visualization." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04417-5_26.

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Tanaka, Hideo, and Peijun Guo. "Possibilistic Data Analysis and Its Similarity to Rough Sets." In Data Mining, Rough Sets and Granular Computing. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1791-1_26.

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Jenhani, 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. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75256-1_73.

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Jia, Kexin, Miao He, and Ting Cheng. "A New Similarity Measure Based Robust Possibilistic C-Means Clustering Algorithm." In Web Information Systems and Mining. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23982-3_41.

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Slokom, Manel, and Raouia Ayachi. "A Hybrid User and Item Based Collaborative Filtering Approach by Possibilistic Similarity Fusion." In Advances in Combining Intelligent Methods. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46200-4_7.

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Conference papers on the topic "Possibilistic similarity"

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"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.

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Zhang, Chunhui, Yiming Zhou, and Trevor Martin. "Similarity Based Fuzzy and Possibilistic c-means Algorithm." In 11th Joint Conference on Information Sciences. Atlantis Press, 2008. http://dx.doi.org/10.2991/jcis.2008.9.

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Skrjanc, 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.

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"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.

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