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Titlebook: Artificial Intelligence: Methods and Applications; 8th Hellenic Confere Aristidis Likas,Konstantinos Blekas,Dimitris Kalle Conference proce

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41#
發(fā)表于 2025-3-28 18:07:38 | 只看該作者
Activity Recognition for Traditional Dances Using Dimensionality Reductiona general dimensionality reduction framework. Experiments on a traditional dance recognition dataset are conducted and the advantage of using dimensionality reduction before classification is highlighted.
42#
發(fā)表于 2025-3-28 19:00:53 | 只看該作者
43#
發(fā)表于 2025-3-28 23:07:38 | 只看該作者
44#
發(fā)表于 2025-3-29 05:05:09 | 只看該作者
Artificial Intelligence: Methods and Applications978-3-319-07064-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
45#
發(fā)表于 2025-3-29 08:33:20 | 只看該作者
Fehlermeldeverhalten in der Pflege propose an algorithm for the propagation of belief functions in the singly-connected directed evidential networks, when each node is associated with one conditional belief function distribution specified given all its parents.
46#
發(fā)表于 2025-3-29 13:40:25 | 只看該作者
Zyklische Codes und CRC-Verfahren,or variable selection and classifier) and tune their hyper-parameters (e.g., K in K-NN), also called ., and (b) provide an estimate of the performance of the final, reported model. Combining the two tasks is not trivial because when one selects the set of hyper-parameters that seem to provide the be
47#
發(fā)表于 2025-3-29 18:48:27 | 只看該作者
Eingangsbeispiele und Blockcodes,ess either from scratch with an empty rule base or from an initially trained fuzzy model. Importantly, pClass not only adopts the open structure concept, where an automatic knowledge building process can be cultivated during the training process, which is well-known as a main pillar to learn from st
48#
發(fā)表于 2025-3-29 23:48:36 | 只看該作者
49#
發(fā)表于 2025-3-30 00:54:54 | 只看該作者
50#
發(fā)表于 2025-3-30 04:30:17 | 只看該作者
Eingangsbeispiele und Blockcodes, Transfer learning comprises a suitable solution for reinforcement learning algorithms, which often require a considerable amount of training time, especially when dealing with complex tasks. This work proposes an autonomous method for transfer learning in reinforcement learning agents. The proposed
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