Results 301 to 310 of about 601,753 (327)

Reducing manual labeling requirements and improved retinal ganglion cell identification in 3D AO-OCT volumes using semi-supervised learning. [PDF]

Biomed Opt Express
Zhou M, Zhang Y, Karimi Monsefi A, Choi SS, Doble N, Parthasarathy S, Ramnath R.   +6 more
europepmc   +1 more source

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools. [PDF]

Nat Methods
Biderman D, Whiteway MR, Hurwitz C, Greenspan N, Lee RS, Vishnubhotla A, Warren R, Pedraja F, Noone D, Schartner MM, Huntenburg JM, Khanal A, Meijer GT, Noel JP, Pan-Vazquez A, Socha KZ, Urai AE, International Brain Laboratory, Cunningham JP, Sawtell NB, Paninski L.   +20 more
europepmc   +1 more source

Longitudinal Cohort Study of Metabolomic Markers Associated With Type 2 Diabetes Risk in Korean Adults

Chronic Diseases and Translational Medicine, EarlyView.
Md. Kamruzzaman, Catherine Apio, Md. Mozaffar Hosain, Min Kyong Moon, Geum‐Sook Hwang, Eunyong Ahn, Taesung Park   +6 more
wiley   +1 more source

UKSSL: Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification. [PDF]

IEEE Open J Eng Med Biol
Ren Z, Kong X, Zhang Y, Wang S.
europepmc   +1 more source

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Interpretable Graph Convolutional Network for Multi-View Semi-Supervised Learning

IEEE transactions on multimedia, 2023
As real-world data become increasingly heterogeneous, multi-view semi-supervised learning has garnered widespread attention. Although existing studies have made efforts towards this and achieved decent performance, they are restricted to shallow models ...
Zhihao Wu, Xincan Lin, Zhenghong Lin, Zhaoliang Chen, Yang Bai, Shiping Wang   +5 more
semanticscholar   +1 more source

Semi-Supervised Learning

2014
Abstract In the world of modern technology, digital data are generated at a lightning speed. These data are typically unlabeled as obtaining labels often requires time-consuming and costly human input. Semi-supervised learning was introduced to study the problem of using the labeled and unlabeled data together to improve learning. Two basic questions
Xueyuan Zhou, Mikhail Belkin
openaire   +3 more sources

Semi-Supervised Learning

2015
In this chapter, we give an overview of different approaches developed in semi-supervised learning, as well as different assumptions leading to these methods. We particularly consider the margin as an indicator of confidence which constitutes the working hypothesis of algorithms that search the decision boundary on low density regions.
Nicolas Usunier, Massih-Reza Amini
openaire   +2 more sources

Semi-supervised learning with an imperfect supervisor

Knowledge and Information Systems, 2005
Real-life applications may involve huge data sets with misclassified or partially classified training data. Semi-supervised learning and learning in the presence of label noise have recently emerged as new paradigms in the machine learning community to cope with this kind of problems.
Amini, Massih-Reza, Gallinari, Patrick
openaire   +3 more sources

Semi-Supervised Learning

2021
We come to the watermelon field during the harvest season, and the ground is covered with many watermelons. The melon farmer brings a handful of melons and says that they are all ripe melons, and then points at a few melons in the ground and says that these are not ripe, and they would take a few more days to grow up.
openaire   +2 more sources