讨厌
发表于 2025-3-28 15:16:08
https://doi.org/10.1007/978-3-658-40601-1stream mining. Existing algorithms exploit either bottom-up or top-down processing strategy to solve this problem, whereas we propose a novel combination of these two strategies. Based on this strategy and a devised compact data structure, we implement our algorithm. It is theoretically proved to ha
松软无力
发表于 2025-3-28 21:22:49
Lisa-Marie Pilz,Tobias Prill,Claudia Kalischn the objective function. Such an addition leads to multiple iterations in the E-step. Besides, the clustering result depends mainly on the choice of the spatial coefficient, which is used to weigh the penalty term but is hard to determine a priori. Furthermore, it may not be appropriate to assign a
countenance
发表于 2025-3-28 23:36:17
Peter Cornelius,Gert-Holger Klevenow (WSNs). The distributed and online learning for target classification is significant for highly-constrained WSNs. This paper presents a collaborative target classification algorithm for image recognition in WSNs, taking advantages of the collaboration for the data mining between multi-sensor nodes.
thalamus
发表于 2025-3-29 05:30:20
Advanced Data Mining and Applications978-3-540-73871-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
chastise
发表于 2025-3-29 09:09:52
https://doi.org/10.1007/978-3-540-73871-8Attribut; Bayesian networks; Business-Intelligence; Fusion; algorithms; bioinformatics; classification; cor
Congregate
发表于 2025-3-29 11:54:16
http://reply.papertrans.cn/15/1455/145497/145497_46.png
Deject
发表于 2025-3-29 18:00:32
http://reply.papertrans.cn/15/1455/145497/145497_47.png
采纳
发表于 2025-3-29 20:11:32
http://reply.papertrans.cn/15/1455/145497/145497_48.png
织布机
发表于 2025-3-30 00:18:10
http://reply.papertrans.cn/15/1455/145497/145497_49.png
陶醉
发表于 2025-3-30 06:09:43
Lisa-Marie Pilz,Tobias Prill,Claudia Kalischiant of NEM using varying coefficients, which are determined by the correlation of explanatory attributes inside the neighborhood. Our experimental results on real data sets show that it only needs one iteration in the E-step and consequently converges faster than NEM. The final clustering quality is also better than NEM.