Results 211 to 220 of about 41,355 (238)

Performance evaluation of semi-supervised learning frameworks for multi-class weed detection. [PDF]

Front Plant Sci
Li J, Chen D, Yin X, Li Z.
europepmc   +1 more source

Enhancing Knowledge and Technology Transfer: Determinants and Indicators of Successful University–Industry Collaboration

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Both universities and companies create value and innovation to maintain their position and remain competitive. Different, but still similar, with two goals that are shared. With their collaboration, they can enhance their pursuit of sustainability and as well corporate social responsibility by creating and delivering value and thus ...
Jana Hojnik, Štefan Bojnec, Simona Kustec, Vaida Zemlickienė, Zenonas Turskis   +4 more
wiley   +1 more source

Semi-supervised learning with flexible threshold for non-intrusive load monitoring. [PDF]

Heliyon
Tang T, Li K, Su C, Liu Z.
europepmc   +1 more source

Semi-supervised Learning for Generalizable Intracranial Hemorrhage Detection and Segmentation. [PDF]

Radiol Artif Intell
Lin E, Yuh EL.
europepmc   +1 more source

Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning. [PDF]

Med Image Anal
Zhao F, Wu Z, Wang L, Lin W, Li G.
europepmc   +1 more source

CACs Recognition of FISH Images Based on Adaptive Mean Teacher Semi-supervised Learning with Domain-Knowledge Pseudo Label. [PDF]

J Imaging Inform Med
Weng Y, Hu Q, Wang H, Kuang Y, Zhou Y, Tang Y, Wang L, Ye X, Lu X.   +8 more
europepmc   +1 more source

A Pseudo-Labeling Multi-Screening-Based Semi-Supervised Learning Method for Few-Shot Fault Diagnosis. [PDF]

Sensors (Basel)
Liu S, Zhu Z, Chen Z, He J, Chen X, Chen Z.   +5 more
europepmc   +1 more source

Modeling disagreement in automatic data labeling for semi-supervised learning in Clinical Natural Language Processing. [PDF]

Front Artif Intell
Liu H, Seedat N, Ive J.
europepmc   +1 more source

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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

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   +2 more sources