Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

笨拙处理

https://doi.org/10.1007/978-3-031-44207-0artificial neural networks (NN); machine learning; deep learning; federated learning; convolutional neur

Middle-Ear

978-3-031-44206-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

遣返回国

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

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

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

Prosaic

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

共同生活

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