Inscrutable
发表于 2025-3-26 22:40:56
Kommentierte Auswahlbibliographie, methods that have been developed to solve these classification problems are neural network (NN) and support vector machine (SVM) classifiers. Despite their successful application to classification problems, these classifiers are limited, in that users must use trial-and error to modify specific par
偏狂症
发表于 2025-3-27 04:26:34
http://reply.papertrans.cn/15/1455/145488/145488_32.png
蚀刻术
发表于 2025-3-27 06:31:11
http://reply.papertrans.cn/15/1455/145488/145488_33.png
CLASP
发表于 2025-3-27 11:21:33
,Bernhard von Gudden in Werneck (1855–1869),te its neighbors. We propose an influence diffusion model called multiple spread model, in which an active node has many activation chances. We prove that influence maximizing problem with the proposed model is submodular and monotone, which means greedy algorithm provides (1-1/e) approximation to o
减至最低
发表于 2025-3-27 14:17:17
http://reply.papertrans.cn/15/1455/145488/145488_35.png
先行
发表于 2025-3-27 21:39:47
http://reply.papertrans.cn/15/1455/145488/145488_36.png
Alcove
发表于 2025-3-27 23:13:14
https://doi.org/10.1007/978-3-540-39721-2ant task in many social networking sites. Traditional content-based and collaborative filtering methods are not sufficient for people-to-people recommendation because a good match depends on the preferences of . sides. We proposed a framework for social recommendation and develop a representation fo
压倒性胜利
发表于 2025-3-28 02:53:32
,Bernhard von Gudden — Der Lebenslauf —,re and content of microgroup (community) on TSina in detail, we reveal that different from ordinary social networks, the degree assortativity coefficients are negative on most microgroups. In addition, we find that users from the same microgroup likely exhibit some similar attributes (e.g., sharing
我悲伤
发表于 2025-3-28 08:43:41
http://reply.papertrans.cn/15/1455/145488/145488_39.png
密切关系
发表于 2025-3-28 11:01:30
Martin Huber,Manfred Mittermayerropose the use of tree pattern mining techniques to discover potentially interesting patterns within longitudinal data sets. Following the approach described in , we propose four different representation schemes for longitudinal studies and we analyze the kinds of patterns that can be identified