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Online learning with ensembles [PDF]
Physical Review E, 2000 Supervised online learning with an ensemble of students randomized by the choice of initial conditions is analyzed. For the case of the perceptron learning rule, asymptotically the same improvement in the generalization error of the ensemble compared to the performance of a single student is found as in Gibbs learning. For more optimized learning rules,
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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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Learning with Pseudo-Ensembles
CoRR, 2014 To appear in Advances in Neural Information Processing Systems 27 (NIPS 2014), Advances in Neural Information Processing Systems 27, Dec ...
Philip Bachman +2 more
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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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Surface Water Quality Classification via CMAES Ensemble Method
Jisuanji kexue yu tansuo, 2020 In order to improve the quality of people’s daily life, the government departments continue to strengthen water quality management. However, artificial classification method cannot meet the needs of real-time processing, additionally the classification ...
CHEN Xingguo, XU Xiuying, CHEN Kangyang, YANG Guang
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Distributional Reinforcement Learning with Ensembles
Algorithms, 2020 It is well known that ensemble methods often provide enhanced performance in reinforcement learning. In this paper, we explore this concept further by using group-aided training within the distributional reinforcement learning paradigm. Specifically, we propose an extension to categorical reinforcement learning, where distributional learning targets ...
Björn Lindenberg +2 more
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Ensemble of SVMs for Incremental Learning [PDF]
, 2005 Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems. However, SVMs suffer from the catastrophic forgetting phenomenon, which results in loss of previously learned information. Learn++ have recently been introduced as an incremental learning algorithm.
Zeki Erdem +3 more
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