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Titlebook: Big Data Analytics and Knowledge Discovery; 25th International C Robert Wrembel,Johann Gamper,Ismail Khalil Conference proceedings 2023 The

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21#
發(fā)表于 2025-3-25 04:39:20 | 只看該作者
22#
發(fā)表于 2025-3-25 07:32:11 | 只看該作者
https://doi.org/10.1007/978-1-4842-5917-7s on the existence of differences between two datasets as contrast ItemSB. We further report the results of evaluation experiments conducted on the properties of ItemSB from the perspective of reproducibility and reliability using contrast ItemSB.
23#
發(fā)表于 2025-3-25 13:57:24 | 只看該作者
Discovery of?Contrast Itemset with?Statistical Background Between Two Continuous Variabless on the existence of differences between two datasets as contrast ItemSB. We further report the results of evaluation experiments conducted on the properties of ItemSB from the perspective of reproducibility and reliability using contrast ItemSB.
24#
發(fā)表于 2025-3-25 18:34:39 | 只看該作者
25#
發(fā)表于 2025-3-25 21:28:13 | 只看該作者
Decision Diagram-Based Simulationriately. Experimental results show that by tuning the parameters of the proposed method appropriately, highly accurate results can be obtained even for large hypergraphs for machine learning tasks such as node label classification.
26#
發(fā)表于 2025-3-26 02:20:33 | 只看該作者
27#
發(fā)表于 2025-3-26 04:34:42 | 只看該作者
28#
發(fā)表于 2025-3-26 08:52:22 | 只看該作者
29#
發(fā)表于 2025-3-26 16:14:25 | 只看該作者
Contextual Shift Method (CSM)icial data point. The problem of not generating contextual shifts is true for the quantile shift method. We propose the Contextual Shift Method (CSM), which improves the quantile shift method by generating contextual shifts. We show that the CSM reduces the amount of data points created in low data density areas.
30#
發(fā)表于 2025-3-26 20:27:02 | 只看該作者
Hypergraph Embedding Based on?Random Walk with?Adjusted Transition Probabilitiesriately. Experimental results show that by tuning the parameters of the proposed method appropriately, highly accurate results can be obtained even for large hypergraphs for machine learning tasks such as node label classification.
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