Results 11 to 20 of about 102,109 (257)
ReliaMatch: Semi-Supervised Classification with Reliable Match
Applied Sciences, 2023 Deep learning has been widely used in various tasks such as computer vision, natural language processing, predictive analysis, and recommendation systems in the past decade.
Tao Jiang +4 more
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SEMI-SUPERVISED MARGINAL FISHER ANALYSIS FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification ...
H. Huang, J. Liu, Y. Pan
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Semi-Supervised Classification Based on Mixture Graph
Algorithms, 2015 Graph-based semi-supervised classification heavily depends on a well-structured graph. In this paper, we investigate a mixture graph and propose a method called semi-supervised classification based on mixture graph (SSCMG).
Lei Feng, Guoxian Yu
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LMGAN: Linguistically Informed Semi-Supervised GAN with Multiple Generators
Sensors, 2022 Semi-supervised learning is one of the active research topics these days. There is a trial that solves semi-supervised text classification with a generative adversarial network (GAN).
Whanhee Cho, Yongsuk Choi
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Remote Sensing, 2023 Few-shot hyperspectral classification is a challenging problem that involves obtaining effective spatial–spectral features in an unsupervised or semi-supervised manner.
Chunyu Li, Rong Cai, Junchuan Yu
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SSBTCNet: Semi-Supervised Brain Tumor Classification Network
IEEE Access, 2023 Classification of brain tumors from the Magnetic Resonance Imaging (MRI) is a vital and challenging task for brain tumor diagnosis. Despite favorable results, from current Deep Learning (DL) methods used for the classification of brain tumors, the ...
Zubair Atha, Jyotismita Chaki
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Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning
IEEE Access, 2022 The annotation of large datasets is often the bottleneck in the successful application of artificial intelligence in computational pathology. For this reason recently Multiple Instance Learning (MIL) and Semi Supervised Learning (SSL) approaches are ...
Arne Schmidt +3 more
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Semi-supervised Learning on Graphs Using Adversarial Training with Generated Sample [PDF]
Jisuanji kexue yu tansuo, 2023 Given a graph composed of a small number of labeled nodes and a large number of unlabeled nodes, semi-supervised learning on graphs aims to assign labels for the unlabeled nodes.
WANG Cong, WANG Jie, LIU Quanming, LIANG Jiye
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Digging Into Pseudo Label: A Low-Budget Approach for Semi-Supervised Semantic Segmentation
IEEE Access, 2020 The capability to understand visual scenes with limited labeled data has been widely concerned in the field of computer vision. Although semi-supervised learning for image classification has been extensively studied in some cases, semantic segmentation ...
Zhenghao Chen +4 more
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Semi-supervised morphosyntactic classification of Old Icelandic. [PDF]
PLoS ONE, 2014 We present IceMorph, a semi-supervised morphosyntactic analyzer of Old Icelandic. In addition to machine-read corpora and dictionaries, it applies a small set of declension prototypes to map corpus words to dictionary entries.
Kryztof Urban +3 more
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