Results 11 to 20 of about 38,536 (261)
Improving Semi-Supervised Classification using Clustering [PDF]
EAI Endorsed Transactions on Scalable Information Systems, 2020 Supervised classification techniques, broadly depend on the availability of labeled data. However, collecting this labeled data is always a tedious and costly process.
J. Arora, M. Tushir, R. Kashyap
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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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Watersheds for Semi-Supervised Classification [PDF]
IEEE Signal Processing Letters, 2019 Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered – How do the boundaries of the watershed operator behave?
Aditya Challa +3 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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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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Augmentation Learning for Semi-Supervised Classification
, 2022 Recently, a number of new Semi-Supervised Learning methods have emerged. As the accuracy for ImageNet and similar datasets increased over time, the performance on tasks beyond the classification of natural images is yet to be explored. Most Semi-Supervised Learning methods rely on a carefully manually designed data augmentation pipeline that is not ...
Tim Frommknecht +4 more
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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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An Exploration of Semi-supervised Text Classification
, 2022 Good performance in supervised text classification is usually obtained with the use of large amounts of labeled training data. However, obtaining labeled data is often expensive and time-consuming. To overcome these limitations, researchers have developed Semi-Supervised learning (SSL) algorithms exploiting the use of unlabeled data, which are ...
Lien, Henrik +3 more
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Semi-Supervised Hierarchical Graph Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph usually represents a real-world entity, e.g., a user in a social network, or a document in a document citation network.
Jia Li 0009 +3 more
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