Results 31 to 40 of about 44,296 (265)
Feature ranking for semi-supervised learning
Machine Learning, 2022 AbstractThe data used for analysis are becoming increasingly complex along several directions: high dimensionality, number of examples and availability of labels for the examples. This poses a variety of challenges for the existing machine learning methods, related to analyzing datasets with a large number of examples that are described in a high ...
Matej Petkovic +2 more
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Towards semi-supervised ensemble clustering using a new membership similarity measure
Automatika, 2023 Hierarchical clustering is a common type of clustering in which the dataset is hierarchically divided and represented by a dendrogram. Agglomerative Hierarchical Clustering (AHC) is a common type of hierarchical clustering in which clusters are created ...
Wenjun Li, Ting Li, Musa Mojarad
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Semi-supervised learning with regularized Laplacian [PDF]
Optimization Methods and Software, 2016 We study a semi-supervised learning method based on the similarity graph and RegularizedLaplacian. We give convenient optimization formulation of the Regularized Laplacian method and establishits various properties. In particular, we show that the kernel of the methodcan be interpreted in terms of discrete and continuous time random walks and possesses
Konstantin Avrachenkov +2 more
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South African Computer Journal, 2020 Semi-supervised learning is a potential solution for improving training data in low-resourced abusive language detection contexts such as South African abusive language detection on Twitter.
Oluwafemi Oriola, Eduan Kotzé
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Driving Maneuver Classification Using Domain Specific Knowledge and Transfer Learning
IEEE Access, 2021 With the increasing number of vehicles, the usage of technology has also been increased in the transportation system. Although automobile companies are using advanced technologies to develop high performing transports, traffic safety still remains to be ...
Supriya Sarker +2 more
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Graph Laplacian for Semi-supervised Learning
, 2023 Semi-supervised learning is highly useful in common scenarios where labeled data is scarce but unlabeled data is abundant. The graph (or nonlocal) Laplacian is a fundamental smoothing operator for solving various learning tasks. For unsupervised clustering, a spectral embedding is often used, based on graph-Laplacian eigenvectors.
Streicher, Or, Gilboa, Guy
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Semi-Supervised Learning with Scarce Annotations [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020 Workshop on Deep Vision, CVPR ...
Rebuffi, S-A +4 more
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Efficiently Learning the Graph for Semi-supervised Learning
CoRR, 2023 29 pages, 9 ...
Dravyansh Sharma, Maxwell Jones
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Dual Learning-Based Safe Semi-Supervised Learning
IEEE Access, 2018 In many real-world applications, labeled instances are generally limited and expensively collected, while the most instances are unlabeled and the amount is often sufficient.
Haitao Gan +3 more
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Semi‐supervised learning dehazing algorithm based on the OSV model
IET Image Processing, 2023 Despite the great progress that has been made in the task of single image dehazing, the results of the existing models in restoring image edge and texture information are still challenging.
Lijun Zhu +5 more
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