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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 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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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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TUMK-ELM: A Fast Unsupervised Heterogeneous Data Learning Approach
IEEE Access, 2018 Advanced unsupervised learning techniques are an emerging challenge in the big data era due to the increasing requirements of extracting knowledge from a large amount of unlabeled heterogeneous data.
Lingyun Xiang +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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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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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 and generalization [PDF]
Proceedings of International Conference on Neural Networks (ICNN'96), 2002 The concept of generalization is defined for a general class of unsupervised learning machines. The generalization error is a straightforward extension of the corresponding concept for supervised learning, and may be estimated empirically using a test set or by statistical means-in close analogy with supervised learning.
Lars Kai Hansen, Jan Larsen
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