笨拙处理
发表于 2025-3-25 04:10:14
https://doi.org/10.1007/978-3-031-44207-0artificial neural networks (NN); machine learning; deep learning; federated learning; convolutional neur
Middle-Ear
发表于 2025-3-25 08:29:19
978-3-031-44206-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
遣返回国
发表于 2025-3-25 13:34:17
G. J. Wullems,J. A. M. Schrauwene. It is biased to evaluate the model’s performance only based on the classifier accuracy while ignoring the data separability. Sometimes, the model exhibits excellent accuracy, which might be attributed to its testing on highly separable data. Most of the current studies on data separability measur
V洗浴
发表于 2025-3-25 16:09:54
G. J. Wullems,J. A. M. Schrauwenhas focused on identifying suitable knowledge and enhancing network structures to obtain more valuable knowledge. However, the introduction of extra information such as semantics remains an unexplored area. In this study, we introduce a multi-label classifier with label embeddings to replace the tra
panorama
发表于 2025-3-25 20:46:30
Graham J. Wishart,A. Janet Horrocksthis problem from the data level or algorithm level. Nevertheless, these methods have their limitations. In addition, most of them focus on dealing with the imbalance in the number of data samples while ignoring the imbalance caused by sample difficulty. Thus, we design a hybrid model to handle this
jealousy
发表于 2025-3-26 03:12:57
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Prosaic
发表于 2025-3-26 05:29:56
https://doi.org/10.1007/978-3-642-68327-5ing a higher benefit, one aims to classify a sequence as accurately as possible, as soon as possible, without having to wait for the last element. For this early sequence classification, we introduce our novel classifier-induced stopping. While previous methods depend on exploration during training
LAVE
发表于 2025-3-26 10:03:05
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共同生活
发表于 2025-3-26 14:13:17
Henning M. Beier,Hans R. Lindnerarity in recent years. In this study, we apply two models: AE-SIS (Analytic Hierarchy Process-Entropy Weight-TOPSIS) and AW-AB (Adjusted Weight in Adaptive Boosting) to evaluate in-class teaching quality. We provide an ensemble scheme for intelligent in-class evaluation that combines the benefits of
Culpable
发表于 2025-3-26 20:08:16
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