Results 271 to 280 of about 238,312 (311)

Migrant success in UK Education: Are there lessons for government social mobility policy?

British Educational Research Journal, EarlyView.
Abstract The school achievement and career aspirations of 23 sixth form students at a multi‐cultural urban academy in the UK are explored through interviews. The sample includes 16 s‐generation migrants, 6 UK‐born students with migrant parents and 1 UK‐born student, selected to represent a cohort of over 300 post‐16 learners.
Bernard Barker, Kate Hoskins
wiley   +1 more source

Oral-Anatomical Knowledge-Informed Semi-Supervised Learning for 3D Dental CBCT Segmentation and Lesion Detection. [PDF]

IEEE Trans Autom Sci Eng
Lee Y, Kwak MG, Chen RQ, Yan H, Mupparapu M, Lure F, Setzer FC, Li J.   +7 more
europepmc   +1 more source

Automatic segmentation and landmark detection of 3D CBCT images using semi supervised learning for assisting orthognathic surgery planning. [PDF]

Sci Rep
Tang H, Liu S, Shi Y, Wei J, Peng J, Feng H.   +5 more
europepmc   +1 more source

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

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

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Semi-supervised learning by disagreement

Knowledge and Information Systems, 2009
In many real-world tasks, there are abundant unlabeled examples but the number of labeled training examples is limited, because labeling the examples requires human efforts and expertise. So, semi-supervised learning which tries to exploit unlabeled examples to improve learning performance has become a hot topic.
Zhi-Hua Zhou, Zhou Zhi-Hua
exaly   +2 more sources

Semi-supervised incremental learning

International Conference on Fuzzy Systems, 2010
The paper introduces a hybrid evolving architecture for dealing with incremental learning. It consists of two components: resource allocating neural network (RAN) and growing Gaussian mixture model (GGMM). The architecture is motivated by incrementality on one hand and on the other hand by the possibility to handle unlabeled data along with the labeled
Abdelhamid Bouchachia, Markus Prossegger, Hakan Duman   +2 more
openaire   +1 more source

Deep Semi-Supervised Learning

2018 24th International Conference on Pattern Recognition (ICPR), 2018
Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large labeled training dataset requires human involvement, which is expensive and time consuming.
Zeyad Hailat, Artem Komarichev, Xue-wen Chen 0001   +2 more
openaire   +1 more source