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- W4386870427 abstract "This study focuses to examine the employ of ensemble methods for improving generalization performance which is the most important in the recent study of machine learning. Ensemble learning combines numerous models and deep learning (DL) models with multi-layered ones and also outperforms deep or classic classification models in terms of performance. Deep ensemble learning methods combine the merits of both ensemble learning and the DL models so that the overall result has better performance. This review analyzes current deep ensemble models and provides a comprehensive overview as Bagging, boosting, and stacking are examples of ensemble models. The negative correlation-based deep ensemble models are explicit or implicit ensembles, homogeneous or heterogeneous ensembles, decision fusion strategies, un-supervised, semi-supervised, reinforcement learning, internet, and multilevel featured ensemble models. Deep ensemble models are used in many fields. The author reviews the recent advancement in ensemble deep learning methods and formulates the research objective at the end of this work and future recommendations." @default.
- W4386870427 created "2023-09-20" @default.
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- W4386870427 date "2023-01-01" @default.
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- W4386870427 title "A Review of Ensemble Methods Used in AI Applications" @default.
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- W4386870427 doi "https://doi.org/10.1007/978-981-99-5080-5_13" @default.
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