Results 11 to 20 of about 222,333 (116)
Generalizable self-supervised learning for imaging flow cytometry on multi-dataset leukocyte differential [PDF]
Microsystems & NanoengineeringImaging flow cytometry with supervised learning can realize high-accuracy leukocyte classification. However, since supervised learning relies on annotated cell images and this labeling process generates cell losses, current imaging flow cytometry with ...
Xukun Huang +11 more
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Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [PDF]
Jisuanji kexue, 2022 Consistency-based semi-supervised learning methods typically use simple data augmentation methods to achieve consistent predictions for both original inputs and perturbed inputs.The effectiveness of this approach is difficult to be guaranteed when the ...
XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo
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Supervised Classification Problems–Taxonomy of Dimensions and Notation for Problems Identification
IEEE Access, 2021 The paper proposes a taxonomy for categorizing the main features of the supervised learning classification problems and a notation for the identification of the supervised learning classification problem categories.
Ireneusz Czarnowski, Piotr Jedrzejowicz
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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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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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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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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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5G/B5G Service Classification Using Supervised Learning
Applied Sciences, 2021 The classification of services in 5G/B5G (Beyond 5G) networks has become important for telecommunications service providers, who face the challenge of simultaneously offering a better Quality of Service (QoS) in their networks and a better Quality of ...
Jorge E. Preciado-Velasco +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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