Results 31 to 40 of about 219,224 (269)
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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Learning to Learn from Weak Supervision by Full Supervision
CoRR, 2017 Accepted at NIPS Workshop on Meta-Learning (MetaLearn 2017), Long Beach, CA ...
Dehghani, M. +3 more
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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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Adversarial Dropout for Supervised and Semi-Supervised Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Recently, training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has improved the generalization performance of neural networks. In contrast to the biased individual inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of ...
Sungrae Park +3 more
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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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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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Longitudinal self-supervised learning [PDF]
Medical Image Analysis, 2021 Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires a large number of ground-truth labels to be informative. As ground-truth labels are often missing or expensive to obtain in neuroscience, we avoid them in our analysis by combing factor disentanglement with self-supervised learning to ...
Qingyu Zhao +3 more
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Journal of the American Statistical Association, 2013 A new machine learning task is introduced, called latent supervised learning, where the goal is to learn a binary classifier from continuous training labels which serve as surrogates for the unobserved class labels. A specific model is investigated where the surrogate variable arises from a two-component Gaussian mixture with unknown means and ...
Susan, Wei, Michael R, Kosorok
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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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