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Titlebook: Machine Learning, Optimization, and Data Science; 6th International Co Giuseppe Nicosia,Varun Ojha,Renato Umeton Conference proceedings 202

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41#
發(fā)表于 2025-3-28 18:30:03 | 只看該作者
Conference proceedings 2020, and Data Science, LOD 2020, held in Siena, Italy, in July 2020...The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine l
42#
發(fā)表于 2025-3-28 22:31:38 | 只看該作者
43#
發(fā)表于 2025-3-28 23:52:01 | 只看該作者
Coking Coal Railway Transportation Forecasting Using Ensembles of ElasticNet, LightGBM, and Faceboortation volumes are best predicted by an ensemble of ElasticNet and LightGBM models, giving the mean absolute percentage error at 10%. The best model for coking coal domestic transportation forecast is pure LightGBM, with the mean absolute percentage error at 6%.
44#
發(fā)表于 2025-3-29 04:49:01 | 只看該作者
Conference proceedings 2020gs set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications..
45#
發(fā)表于 2025-3-29 09:10:50 | 只看該作者
Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing G explicit calculation of gradients or adjoints and we show this resolves the exploding and vanishing gradient problem. We analyze different parameter initializations, propose a dynamic change in ensembles and compare results to established methods.
46#
發(fā)表于 2025-3-29 12:46:08 | 只看該作者
47#
發(fā)表于 2025-3-29 15:53:02 | 只看該作者
48#
發(fā)表于 2025-3-29 20:14:01 | 只看該作者
49#
發(fā)表于 2025-3-30 01:51:39 | 只看該作者
50#
發(fā)表于 2025-3-30 04:53:17 | 只看該作者
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