Results 21 to 30 of about 242,529 (275)
Review of Self-supervised Learning Methods in Field of ECG [PDF]
Jisuanji kexue yu tansuoDeep learning has been widely applied in the field of electrocardiogram (ECG) signal analysis due to its powerful data representation capability. However, supervised methods require a large amount of labeled data, and ECG data annotation is typically ...
HAN Han, HUANG Xunhua, CHANG Huihui, FAN Haoyi, CHEN Peng, CHEN Jijia
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Building One-Shot Semi-Supervised (BOSS) Learning Up to Fully Supervised Performance
Frontiers in Artificial Intelligence, 2022 Reaching the performance of fully supervised learning with unlabeled data and only labeling one sample per class might be ideal for deep learning applications.
Leslie N. Smith, Adam Conovaloff
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Geostatistical semi-supervised learning for spatial prediction
Artificial Intelligence in Geosciences, 2022 Geoscientists are increasingly tasked with spatially predicting a target variable in the presence of auxiliary information using supervised machine learning algorithms.
Francky Fouedjio, Hassan Talebi
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Remote Sensing, 2022 In recent years, supervised learning, represented by deep learning, has shown good performance in remote sensing image scene classification with its powerful feature learning ability. However, this method requires large-scale and high-quality handcrafted
Xiliang Chen, Guobin Zhu, Mingqing Liu
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Cross-supervised learning for cloud detection
GIScience & Remote Sensing, 2023 We present a new learning paradigm, that is, cross-supervised learning, and explore its use for cloud detection. The cross-supervised learning paradigm is characterized by both supervised training and mutually supervised training, and is performed by two
Kang Wu +3 more
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DenseCL: A simple framework for self-supervised dense visual pre-training
Visual Informatics, 2023 Self-supervised learning aims to learn a universal feature representation without labels. To date, most existing self-supervised learning methods are designed and optimized for image classification.
Xinlong Wang +3 more
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Supervised Machine Learning a Brief Survey of Approaches
Al-Iraqia Journal for Scientific Engineering Research, 2023 Machine learning has become popular across several disciplines right now. It enables machines to automatically learn from data and make predictions without the need for explicit programming or human intervention. Supervised machine learning is a popular
Esraa Najjar, Aqeel Majeed Breesam
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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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FACETSSelf-supervised learning enables the creation of algorithms that outperform supervised pre-training methods in numerous computer vision tasks. This paper provides a comprehensive overview of self-supervised learning applications across various X-ray ...
Ivan Martinović +6 more
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Predicting rank for scientific research papers using supervised learning
Applied Computing and Informatics, 2019 Automatic data processing represents the future for the development of any system, especially in scientific research. In this paper, we describe one of the automatic classification methods applied to scientific research as a supervised learning task ...
Mohamed El Mohadab +2 more
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