Results 31 to 40 of about 752,283 (330)
Classification Uncertainty Minimization-based Semi-supervised Ensemble Learning Algorithm [PDF]
Jisuanji kexue, 2023 Semi-supervised ensemble learning(SSEL) is a combinatorial paradigm by fusing semi-supervised learning and ensemble learning together,which improves the diversity of ensemble learning by introducing unlabeled samples and at the same time solves the ...
HE Yulin, ZHU Penghui, HUANG Zhexue, Fournier-Viger PHILIPPE
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Semisupervised Center Loss for Remote Sensing Image Scene Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 High-resolution remote sensing image scene classification is a scene-level classification task. Driven by a wide range of applications, accurate scene annotation has become a hot and challenging research topic.
Jun Zhang +3 more
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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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5G/B5G Service Classification Using Supervised Learning
Applied Sciences, 2021 The classification of services in 5G/B5G (Beyond 5G) networks has become important for telecommunications service providers, who face the challenge of simultaneously offering a better Quality of Service (QoS) in their networks and a better Quality of ...
Jorge E. Preciado-Velasco +4 more
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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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Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees [PDF]
, 2016 This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi ...
Mu, Xin, Ting, Kai Ming, Zhou, Zhi-Hua
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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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Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning [PDF]
, 2015 In this article we present the supervised iterative projections and rotations (s-ipr) algorithm, a method for learning discriminative incoherent subspaces from data.
D Barchiesi +8 more
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Out-of-sample generalizations for supervised manifold learning for classification [PDF]
, 2015 Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving way, while enhancing the separation between different classes in the ...
Guillemot, Christine, Vural, Elif
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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 +3 more
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