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Titlebook: KI 2017: Advances in Artificial Intelligence; 40th Annual German C Gabriele Kern-Isberner,Johannes Fürnkranz,Matthias Conference proceeding

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樓主: ARGOT
21#
發(fā)表于 2025-3-25 05:21:11 | 只看該作者
22#
發(fā)表于 2025-3-25 09:32:06 | 只看該作者
Propagating Maximum Capacities for Recommendationnappropriately poor performance. As a remedy, we propose to propagate information (e.g., similarities) across the item graph to leverage sparse data. Instead of processing only directly connected items (e.g. co-occurrences), the similarity of two items is defined as the maximum capacity path interco
23#
發(fā)表于 2025-3-25 15:02:24 | 只看該作者
24#
發(fā)表于 2025-3-25 16:25:31 | 只看該作者
25#
發(fā)表于 2025-3-25 20:05:40 | 只看該作者
26#
發(fā)表于 2025-3-26 02:54:19 | 只看該作者
Real-Time Public Transport Delay Prediction for Situation-Aware Routingof real-time data and its predictions in the trip planning process. We present a system for individual multi-modal trip planning that incorporates predictions of future public transport delays in routing. Future delay times are computed by a Spatio-Temporal-Random-Field based on a stream of current
27#
發(fā)表于 2025-3-26 06:22:49 | 只看該作者
A Generalization of Probabilistic Argumentation with Dempster-Shafer Theoryymemes. In this paper, using legal argument from analogy, we argue that in this reconstruction process interpreters may have to deal with a kind of uncertainty that can be appropriately represented in Dempster-Shafer (DS) theory rather than classical probability theory. Hence we generalize and relax
28#
發(fā)表于 2025-3-26 09:22:55 | 只看該作者
29#
發(fā)表于 2025-3-26 15:20:01 | 只看該作者
An Experimental Study of Dimensionality Reduction Methodsance of DR techniques for data analysis and visualization led to the development of a large diversity of DR methods. The lack of comprehensive comparative studies makes it difficult to choose the best DR methods for a particular task based on known strengths and weaknesses. To close the gap, this pa
30#
發(fā)表于 2025-3-26 17:59:23 | 只看該作者
Planning with Independent Task Networksms that can be operationalized using classical graph search techniques such as A*. For two reasons, however, this strategy is sometimes inefficient. First, identical tasks might be resolved several times within the search process, i.e., the same subproblem is solved repeatedly instead of being reuse
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