Results 1 to 10 of about 90,474 (158)
Unsupervised Learning of Morphology [PDF]
Computational Linguistics, 2021 This article surveys work on Unsupervised Learning of Morphology. We define Unsupervised Learning of Morphology as the problem of inducing a description (of some kind, even if only morpheme-segmentation) of how orthographic words are built up given only raw text data of a language.
Harald Hammarström, Lars Borin
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Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review
Sensors, 2023 Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades.
Kareem Eltouny +2 more
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Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
IEEE Access, 2019 While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning.
Muhammad Usama +7 more
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Unsupervised Feature-Learning for Hyperspectral Data with Autoencoders
Remote Sensing, 2019 This paper proposes novel autoencoders for unsupervised feature-learning from hyperspectral data. Hyperspectral data typically have many dimensions and a significant amount of variability such that many data points are required to represent the ...
Lloyd Windrim +4 more
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American Journal of Orthodontics and Dentofacial Orthopedics, 2023 This work was supported by the Flemish Government under the “Onderzoeksprogramma Artifici€ele Intelligentie (AI) Vlaanderen ...
Dirk Valkenborg +3 more
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DLUT: Decoupled Learning-Based Unsupervised Tracker
Sensors, 2023 Unsupervised learning has shown immense potential in object tracking, where accurate classification and regression are crucial for unsupervised trackers.
Zhengjun Xu +4 more
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Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
Algorithms, 2022 Decision support systems with machine learning can help organizations improve operations and lower costs with more precision and efficiency. This work presents a review of state-of-the-art machine learning algorithms for binary classification and makes a
Hugo Silva, Jorge Bernardino
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Contrastive Learning Based on Transformer for Hyperspectral Image Classification
Applied Sciences, 2021 Recently, deep learning has achieved breakthroughs in hyperspectral image (HSI) classification. Deep-learning-based classifiers require a large number of labeled samples for training to provide excellent performance.
Xiang Hu +4 more
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Unsupervised Learning for Parametric Optimization [PDF]
IEEE Communications Letters, 2021 This work was supported by the European Research Council under the H2020 Framework Programme/ERC grant agreement 694974, by the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) as well as by MINECO’s Projects RTI2018-102112 and RTI2018-101040, and by the ICREA Academia program.
Rasoul Nikbakht +2 more
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IEEE Access, 2023 In order to solve the resource allocation problem in scenarios of centralized wireless cellular communication with multiple cells, users and channels, a novel resource allocation algorithm based on joint Deep Deterministic Policy Gradient (DDPG ...
Ming Sun +3 more
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