Results 41 to 50 of about 5,782,720 (331)
Eng, 2023 This paper introduces a novel approach to leveraging features learned from both supervised and self-supervised paradigms, to improve image classification tasks, specifically for vehicle classification.
Shihan Ma, Jidong J. Yang
doaj +1 more source
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer [PDF]
Medical Image Anal., 2021 We propose a Deep learning-based weak label learning method for analyzing whole slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tumor tissue not requiring pixel-level or tile-level annotations using Self-supervised pre-training and ...
Yoni Schirris +4 more
semanticscholar +1 more source
Text Classification with Negative Supervision [PDF]
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020 Advanced pre-trained models for text representation have achieved state-of-the-art performance on various text classification tasks. However, the discrepancy between the semantic similarity of texts and labelling standards affects classifiers, i.e. leading to lower performance in cases where classifiers should assign different labels to semantically ...
Sora Ohashi +4 more
openaire +2 more sources
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
doaj +1 more source
Active metric learning for supervised classification [PDF]
Computers & Chemical Engineering, 2021 Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixed-integer optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extensively studied in the literature.
Krishnan Kumaran +4 more
openaire +3 more sources
General fuzzy min-max neural network for clustering and classification [PDF]
, 2000 This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993).
Gabrys, Bogdan, Bargiela, Andrzej
core +1 more source
Self-Supervised EEG Emotion Recognition Models Based on CNN
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023 Emotion plays crucial roles in human life. Recently, emotion classification from electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid development of brain computer interface (BCI) techniques and machine learning ...
Xingyi Wang +5 more
doaj +1 more source
Deep graph learning for semi-supervised classification [PDF]
Pattern Recognition, 2020 Graph learning (GL) can dynamically capture the distribution structure (graph structure) of data based on graph convolutional networks (GCN), and the learning quality of the graph structure directly influences GCN for semi-supervised classification ...
Guangfeng Lin +4 more
semanticscholar +1 more source
Supervised classification of human microbiota [PDF]
FEMS Microbiology Reviews, 2011 Recent advances in DNA sequencing technology have allowed the collection of high-dimensional data from human-associated microbial communities on an unprecedented scale. A major goal of these studies is the identification of important groups of microorganisms that vary according to physiological or disease states in the host, but the incidence of rare ...
Dan, Knights +2 more
openaire +2 more sources
Remote Sensing, 2021 The size of the training data set is a major determinant of classification accuracy. Nevertheless, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be ...
Christopher A. Ramezan +3 more
semanticscholar +1 more source