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Online Learning with Ensembles [PDF]
Physical Review E, 1999 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 ...
G. Reents +8 more
core +3 more sources
A comprehensive review on ensemble deep learning: Opportunities and challenges
Journal of King Saud University: Computer and Information Sciences, 2023 In machine learning, two approaches outperform traditional algorithms: ensemble learning and deep learning. The former refers to methods that integrate multiple base models in the same framework to obtain a stronger model that outperforms them.
Ammar Mohammed, Rania Kora
doaj +3 more sources
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
doaj +3 more sources
Multistrategy ensemble learning: reducing error by combining ensemble learning techniques [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2004 Ensemble learning strategies, especially boosting and bagging decision trees, have demonstrated impressive capacities to improve the prediction accuracy of base learning algorithms. Further gains have been demonstrated by strategies that combine simple ensemble formation approaches.
Geoffrey I Webb, Z Zheng
exaly +2 more sources
Frontiers in Pediatrics, 2023 BackgroundExecutive functions (EFs) are a set of neuropsychological skills permitting solving problems in a new situation by regulating action, behavior, and emotional response. As cerebral maturation remains vulnerable in preterm children, a higher risk
Magali Reynold de Seresin +17 more
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Energies, 2023 Accurate multivariate load forecasting plays an important role in the planning management and safe operation of integrated energy systems. In order to simultaneously reduce the prediction bias and variance, a hybrid ensemble learning method for load ...
Wenxia You +3 more
doaj +1 more source
CNN Ensemble learning method for Transfer learning: A Review
Ilkom Jurnal Ilmiah, 2023 This study provides a review of CNN's ensemble learning method for transfer learning by highlighting sections such as review studies, datasets, pre-trained models, transfer learning, ensemble learning, and performance.
Yudha Islami Sulistya +2 more
doaj +1 more source
Complementary Ensemble Learning
CoRR, 2021 To achieve high performance of a machine learning (ML) task, a deep learning-based model must implicitly capture the entire distribution from data. Thus, it requires a huge amount of training samples, and data are expected to fully present the real distribution, especially for high dimensional data, e.g., images, videos.
Hung Nguyen 0008, J. Morris Chang
openaire +2 more sources
Driver Distraction Classification Using Deep Convolutional Autoencoder and Ensemble Learning
IEEE Access, 2023 The study of real-time classification for driver distraction provides new insights into the understanding of behavioral and cognitive reasons behind it. Among various approaches, deep learning models show better performance and can be utilized for a real-
Anirudh Muthuswamy +3 more
doaj +1 more source
Ensemble machine learning methods in screening electronic health records: A scoping review
Digital Health, 2023 Background Electronic health records provide the opportunity to identify undiagnosed individuals likely to have a given disease using machine learning techniques, and who could then benefit from more medical screening and case finding, reducing the ...
Christophe AT Stevens +6 more
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