Results 11 to 20 of about 537,050 (171)
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects
IEEE Access, 2022 Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models.
Ibomoiye Domor Mienye, Yanxia Sun
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Domain Adaptive Ensemble Learning [PDF]
IEEE Transactions on Image Processing, 2021 The problem of generalizing deep neural networks from multiple source domains to a target one is studied under two settings: When unlabeled target data is available, it is a multi-source unsupervised domain adaptation (UDA) problem, otherwise a domain generalization (DG) problem.
Kaiyang Zhou +3 more
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Short-Term Load Forecasting Based on Multi-Scale Ensemble Deep Learning Neural Network
IEEE Access, 2023 High-precision load forecasting is crucial for the power system planning and electricity market transactions. Recently, deep learning models have been widely used due to their powerful data mining capabilities. However, the existing research mainly focus
Qin Shen +5 more
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A review on rainfall forecasting using ensemble learning techniques
e-Prime: Advances in Electrical Engineering, Electronics and Energy, 2023 Significant challenges to human health and life have arisen as a result of heavy rains. Floods and other natural disasters that affect people all over the world every year are caused by prolonged periods of heavy rainfall. Predictions of rainfall must be
Saranagata Kundu +5 more
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Analysis of ensemble learning using simple perceptrons based on online learning theory [PDF]
, 2004 Ensemble learning of $K$ nonlinear perceptrons, which determine their outputs by sign functions, is discussed within the framework of online learning and statistical mechanics.
A. Engel +9 more
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A Double Penalty Model for Ensemble Learning
Mathematics, 2022 Modern statistical learning techniques often include learning ensembles, for which the combination of multiple separate prediction procedures (ensemble components) can improve prediction accuracy.
Wenjia Wang, Yi-Hui Zhou
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Machine Learning: Science and Technology, 2023 Multi-label classification (MLC) of aerial images is a crucial task in remote sensing image analysis. Traditional image classification methods have limitations in image feature extraction, leading to an increasing use of deep learning models, such as ...
Ming-Hseng Tseng
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Ensemble Multifeatured Deep Learning Models and Applications: A Survey
IEEE Access, 2023 Ensemble multifeatured deep learning methodology has emerged as a powerful approach to overcome the limitations of single deep learning models in terms of generalization, robustness, and performance.
Satheesh Abimannan +5 more
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A new ensemble learning approach to detect malaria from microscopic red blood cell images
Sensors International, 2023 Malaria is a life-threatening parasitic disease spread by infected female Anopheles mosquitoes. After analyzing it, microscopists detect this disease from the sample of microscopic red blood cell images. A professional microscopist is required to conduct
Mosabbir Bhuiyan, Md Saiful Islam
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A survey on evolutionary ensemble learning algorithm
智能科学与技术学报, 2021 Evolutionary ensemble learning integrates advantages of ensemble learning and evolutionary algorithm and is widely used in machine learning, data mining, and pattern recognition.Firstly, the theoretical basis, formation, and taxonomy are introduced ...
Yi HU +4 more
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