Results 31 to 40 of about 315,285 (319)
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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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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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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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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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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CoRR, 2017 Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation. Two dual tasks have intrinsic connections with each other due to the probabilistic correlation between their models.
Yingce Xia +5 more
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Supervised learning and Co-training [PDF]
Theoretical Computer Science, 2011 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Malte Darnstädt +2 more
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Supervised-learning-Based QoE Prediction of Video Streaming in Future Networks: A Tutorial with Comparative Study [PDF]
, 2021 Quality of experience (QoE)-based service management remains key for successful provisioning of multimedia services in next-generation networks such as 5G/6G, which requires proper tools for quality monitoring, prediction, and resource management where ...
Arslan Ahmad (10136855) +5 more
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Klasifikasi Indeks Pembangunan Gender Di Indonesia Tahun 2020 Menggunakan Supervised Machine Learning Algorithms [PDF]
, 2021 Indeks Pembangunan Gender (IPG) merupakan indikator yang digunakan untuk menggambarkan kesenjangan pencapaian pembangunan manusia antara laki-laki dan perempuan. Capaian IPG Indonesia pada tahun 2020 sebesar 91,06.
Fitriani, Fenny; Program Studi Statistika, Fakultas Sains dan Teknologi, Universitas PGRI Adi Buana Surabaya +2 more
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