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A review of semi-supervised learning for text classification
Artificial Intelligence Review, 2023 A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or ...
José Márcio Duarte, Lilian Berton
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Remote Sensing, 2023 Few-shot hyperspectral classification is a challenging problem that involves obtaining effective spatial–spectral features in an unsupervised or semi-supervised manner.
Chunyu Li, Rong Cai, Junchuan Yu
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COMPARISON OF SEVERAL REMOTE SENSING IMAGE CLASSIFICATION METHODS BASED ON ENVI [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 With the development of remote sensing technology and the increasing accuracy of remote sensing images, research on the accuracy of remote sensing classification is becoming more and more important.
X. C. Li +6 more
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A review on graph-based semi-supervised learning methods for hyperspectral image classification
Egyptian Journal of Remote Sensing and Space Sciences, 2020 In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial ...
Shrutika S. Sawant, Manoharan Prabukumar
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SEMI-SUPERVISED MARGINAL FISHER ANALYSIS FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification ...
H. Huang, J. Liu, Y. Pan
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Supervised classification enables rapid annotation of cell atlases. [PDF]
Nat Methods, 2019 Single-cell molecular profiling technologies are gaining rapid traction, but the manual process by which resulting cell types are typically annotated is labor intensive and rate-limiting.
Pliner HA, Shendure J, Trapnell C.
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ReliaMatch: Semi-Supervised Classification with Reliable Match
Applied Sciences, 2023 Deep learning has been widely used in various tasks such as computer vision, natural language processing, predictive analysis, and recommendation systems in the past decade.
Tao Jiang +4 more
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Classification Uncertainty Minimization-based Semi-supervised Ensemble Learning Algorithm [PDF]
Jisuanji kexue, 2023 Semi-supervised ensemble learning(SSEL) is a combinatorial paradigm by fusing semi-supervised learning and ensemble learning together,which improves the diversity of ensemble learning by introducing unlabeled samples and at the same time solves the ...
HE Yulin, ZHU Penghui, HUANG Zhexue, Fournier-Viger PHILIPPE
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Semisupervised Center Loss for Remote Sensing Image Scene Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 High-resolution remote sensing image scene classification is a scene-level classification task. Driven by a wide range of applications, accurate scene annotation has become a hot and challenging research topic.
Jun Zhang +3 more
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Eng, 2023 This paper introduces a novel approach to leveraging features learned from both supervised and self-supervised paradigms, to improve image classification tasks, specifically for vehicle classification.
Shihan Ma, Jidong J. Yang
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