Добірка наукової літератури з теми "Classification supervisée possibiliste"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Classification supervisée possibiliste".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Classification supervisée possibiliste":
Biondi, Riccardo, Nico Curti, Francesca Coppola, Enrico Giampieri, Giulio Vara, Michele Bartoletti, Arrigo Cattabriga, et al. "Classification Performance for COVID Patient Prognosis from Automatic AI Segmentation—A Single-Center Study." Applied Sciences 11, no. 12 (June 11, 2021): 5438. http://dx.doi.org/10.3390/app11125438.
Madhu, Anjali, Anil Kumar, and Peng Jia. "Exploring Fuzzy Local Spatial Information Algorithms for Remote Sensing Image Classification." Remote Sensing 13, no. 20 (October 18, 2021): 4163. http://dx.doi.org/10.3390/rs13204163.
Amiryousefi, Ali, Ville Kinnula, and Jing Tang. "Bayes in Wonderland! Predictive Supervised Classification Inference Hits Unpredictability." Mathematics 10, no. 5 (March 5, 2022): 828. http://dx.doi.org/10.3390/math10050828.
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 (June 15, 2019): 51–56. http://dx.doi.org/10.32732/jmo.2019.11.1.51.
Jankowski, Maciej. "Deep Generative Model with Supervised Latent Space for Text Classification." MATEC Web of Conferences 292 (2019): 03009. http://dx.doi.org/10.1051/matecconf/201929203009.
Adamiak, Krzysztof, Piotr Duch, and Krzysztof Ślot. "Object Classification Using Support Vector Machines with Kernel-based Data Preprocessing." Image Processing & Communications 21, no. 3 (September 1, 2016): 45–53. http://dx.doi.org/10.1515/ipc-2016-0015.
Jing-Yu, Chen, and Wang Ya-Jun. "Semi-Supervised Fake Reviews Detection based on AspamGAN." March 2022 4, no. 1 (March 30, 2022): 17–36. http://dx.doi.org/10.36548/jaicn.2022.1.002.
Babushka, Andriy, Lyubov Babiy, Borys Chetverikov, and Andriy Sevruk. "GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY." GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY 94, 2021, no. 94 (December 28, 2021): 35–43. http://dx.doi.org/10.23939/istcgcap2021.94.035.
Mehta, Kushal, Arshita Jain, Jayalakshmi Mangalagiri, Sumeet Menon, Phuong Nguyen, and David R. Chapman. "Lung Nodule Classification Using Biomarkers, Volumetric Radiomics, and 3D CNNs." Journal of Digital Imaging 34, no. 3 (February 2, 2021): 647–66. http://dx.doi.org/10.1007/s10278-020-00417-y.
Angulo-Saucedo, Gilbert A., Jersson X. Leon-Medina, Wilman Alonso Pineda-Muñoz, Miguel Angel Torres-Arredondo, and Diego A. Tibaduiza. "Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring." Sensors 22, no. 4 (February 15, 2022): 1484. http://dx.doi.org/10.3390/s22041484.
Дисертації з теми "Classification supervisée possibiliste":
Ben, marzouka Wissal. "Traitement possibiliste d'images, application au recalage d'images." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2022. http://www.theses.fr/2022IMTA0271.
In this work, we propose a possibilistic geometric registration system that merges the semantic knowledge and the gray level knowledge of the images to be registered. The existing geometric registration methods are based on an analysis of the knowledge at the level of the sensors during the detection of the primitives as well as during the matching. The evaluation of the results of these geometric registration methods has limits in terms of the perfection of the precision caused by the large number of outliers. The main idea of our proposed approach is to transform the two images to be registered into a set of projections from the original images (source and target). This set is composed of images called “possibility maps”, each map of which has a single content and presents a possibilistic distribution of a semantic class of the two original images. The proposed geometric registration system based on the possibility theory presents two contexts: a supervised context and an unsupervised context. For the first case, we propose a supervised classification method based on the theory of possibilities using learning models. For the unsupervised context, we propose a possibilistic clustering method using the FCM-multicentroid method. The two proposed methods provide as a result the sets of semantic classes of the two images to be registered. We then create the knowledge bases for the proposed possibilistic registration system. We have improved the quality of the existing geometric registration in terms of precision perfection, reductionin the number of false landmarks and optimization of time complexity
Частини книг з теми "Classification supervisée possibiliste":
Guidolin, Massimo, and Manuela Pedio. "Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms." In Data Science for Economics and Finance, 89–115. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_5.
"Classification Algorithms and Control-Flow Implementation." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 14–45. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8350-0.ch002.
Gharehbaghi, Arash, and Ankica Babic. "A-Test Method for Quantifying Structural Risk and Learning Capacity of Supervised Machine Learning Methods." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti210876.
Salhi, Dhai Eddine, Abelkamel Tari, and Mohand Tahar Kechadi. "Using E-Reputation for Sentiment Analysis." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, 1384–400. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch071.
Brezani, S., R. Hrasko, D. Vanco, J. Vojtas, and P. Vojtas. "Deep Learning for Knowledge Extraction from UAV Images1." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia210476.
Тези доповідей конференцій з теми "Classification supervisée possibiliste":
Mai, Dinh Sinh, and Long Thanh Ngo. "General Semi-supervised Possibilistic Fuzzy c-Means clustering for Land-cover Classification." In 2019 11th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2019. http://dx.doi.org/10.1109/kse.2019.8919476.
Pota, Marco, Massimo Esposito, and Giuseppe De Pietro. "Hybridization of possibility theory and supervised clustering to build DSSs for classification in medicine." In 2012 12th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2012. http://dx.doi.org/10.1109/his.2012.6421383.
Mai, Dinh-Sinh, and Long Thanh Ngo. "Semi-supervised Method with Spatial Weights based Possibilistic Fuzzy c-Means Clustering for Land-cover Classification." In 2018 5th NAFOSTED Conference on Information and Computer Science (NICS). IEEE, 2018. http://dx.doi.org/10.1109/nics.2018.8606801.
Mai, Dinh-Sinh, Long Thanh Ngo, and Le-Hung Trinh. "Advanced Semi-Supervised Possibilistic Fuzzy C-means Clustering Using Spatial-Spectral Distance for Land-Cover Classification." In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00739.
Kheddam, Radja, and Aichouche Belhadj-Aissa. "Possibility theory for supervised classification of remotely sensed images: A study case in an urban area in Algeria." In 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2014. http://dx.doi.org/10.1109/socpar.2014.7007983.
Sitjar Suñer, Josep. "Design and methodology for a remote sensing course." In Symposium on Space Educational Activities (SSAE). Universitat Politècnica de Catalunya, 2022. http://dx.doi.org/10.5821/conference-9788419184405.007.