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Titlebook: Applications of Supervised and Unsupervised Ensemble Methods; Oleg Okun,Giorgio Valentini Book 2009 Springer-Verlag Berlin Heidelberg 2009

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樓主: obsess
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發(fā)表于 2025-3-28 15:26:22 | 只看該作者
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發(fā)表于 2025-3-29 01:23:21 | 只看該作者
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發(fā)表于 2025-3-29 06:35:05 | 只看該作者
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發(fā)表于 2025-3-29 07:54:16 | 只看該作者
Book 2009eld on 21-22 July, 2008 in Patras, Greece, in conjunction with the 18th European Conference on Artificial Intelligence (ECAI’2008). This workshop was a successor of the smaller event held in 2007 in conjunction with 3rd Iberian Conference on Pattern Recognition and Image Analysis, Girona, Spain. The
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發(fā)表于 2025-3-29 12:48:36 | 只看該作者
https://doi.org/10.1007/978-981-13-5983-5vised learning in order to use both labeled and unlabeled samples is explored. The efficiency of the method is evaluated on various UCI datasets and on the classification of a very high resolution remote sensing image when the number of labeled samples is very low.
47#
發(fā)表于 2025-3-29 18:01:45 | 只看該作者
48#
發(fā)表于 2025-3-29 20:41:23 | 只看該作者
Energy Consumption and Autonomous Drivingresentative methods of each category. It abstracts their key components and discusses their main advantages and disadvantages. We hope that this work will serve as a good starting point and reference for researchers working on the development of new ensemble pruning methods.
49#
發(fā)表于 2025-3-30 02:52:14 | 只看該作者
https://doi.org/10.1007/978-981-13-5983-5 during the partitioning process. The experimental results show that our constrained ensemble technique is capable of producing a partition that is as good as, or better, than those computed by other semi-supervised clustering approaches.
50#
發(fā)表于 2025-3-30 07:38:23 | 只看該作者
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