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Titlebook: Letters across Borders; The Epistolary Pract Bruce S. Elliott (Professor of History),David A. G Book 2006 Bruce S. Elliott, David A. Gerber

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21#
發(fā)表于 2025-3-25 05:59:41 | 只看該作者
.The book gathers selected papers presented at the 17th “Transport Systems. Theory and Practice” Scientific and Technical Conference organised by the Department of Transport Systems, Traffic Engineering and Log978-3-030-91155-3978-3-030-91156-0Series ISSN 2367-3370 Series E-ISSN 2367-3389
22#
發(fā)表于 2025-3-25 08:15:20 | 只看該作者
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發(fā)表于 2025-3-25 13:43:36 | 只看該作者
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發(fā)表于 2025-3-25 19:12:42 | 只看該作者
25#
發(fā)表于 2025-3-25 23:49:03 | 只看該作者
Wolfgang Helbich,Walter D. Kamphoefnersed on the ratio of class labels in a leaf node. They select the class label which has the highest proportion of the leaf node. However, when it is not easy to classify dataset according to class labels, leaf nodes includes a lot of data items and class labels. It causes to decrease the accuracy rat
26#
發(fā)表于 2025-3-26 04:00:46 | 只看該作者
27#
發(fā)表于 2025-3-26 05:13:32 | 只看該作者
28#
發(fā)表于 2025-3-26 09:10:23 | 只看該作者
a network of RA2DL components, we propose a coordination method between them using well-defined matrices to allow feasible and coherent reconfigurations. A tool is developed to simulate our approach. All the contributions of this work are applied to a case study dealing with IEEE 802.11 Wireless LAN
29#
發(fā)表于 2025-3-26 13:09:05 | 只看該作者
David Fitzpatrickormance of the proposed method that based on Term Frequency - Inverse Document Frequency (TFIDF) as feature selection method on one hand, while Random Projection (RP) and Principal Component Analysis (PCA) feature selection methods on the other hand. Classification results using the Support Vector M
30#
發(fā)表于 2025-3-26 20:08:46 | 只看該作者
Daiva Markelisormance of the proposed method that based on Term Frequency - Inverse Document Frequency (TFIDF) as feature selection method on one hand, while Random Projection (RP) and Principal Component Analysis (PCA) feature selection methods on the other hand. Classification results using the Support Vector M
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