Results 21 to 30 of about 223,210 (262)
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
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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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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
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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
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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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Supervised Collective Classification for Crowdsourcing [PDF]
2015 IEEE Globecom Workshops (GC Wkshps), 2015 Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data.
Pin-Yu Chen +4 more
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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
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Weakly supervised classification in high energy physics
Journal of High Energy Physics, 2017 As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations.
Lucio Mwinmaarong Dery +3 more
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Generalized Maximum Entropy for Supervised Classification [PDF]
IEEE Transactions on Information Theory, 2022 The maximum entropy principle advocates to evaluate events' probabilities using a distribution that maximizes entropy among those that satisfy certain expectations' constraints. Such principle can be generalized for arbitrary decision problems where it corresponds to minimax approaches.
Santiago Mazuelas +2 more
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Remote Sensing, 2022 Deep learning methods have become an integral part of computer vision and machine learning research by providing significant improvement performed in many tasks such as classification, regression, and detection. These gains have been also observed in the
Paul Berg, Minh-Tan Pham, Nicolas Courty
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