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Titlebook: Data Science; Second International Chengqi Zhang,Wei Huang,Jing He Conference proceedings 2015 Springer International Publishing Switzerlan

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31#
發(fā)表于 2025-3-26 21:37:42 | 只看該作者
32#
發(fā)表于 2025-3-27 02:33:10 | 只看該作者
Active Class Discovery by Querying Pairwise Label Homogeneity,s in the data, a Minimum Spanning Tree based query strategy is employed to discover a number of classes from unlabeled data. Experiments and comparisons demonstrate superior performance of the proposed method for class discovery tasks.
33#
發(fā)表于 2025-3-27 05:57:09 | 只看該作者
34#
發(fā)表于 2025-3-27 10:28:55 | 只看該作者
https://doi.org/10.1007/978-1-4020-9143-8o the theory of metastable, confirm random walk step length .. Finally, achieve community partition of signed network on the basis of the maximum network modularity. Experiments show that the effect of community partition of this method is better than existing methods.
35#
發(fā)表于 2025-3-27 16:55:50 | 只看該作者
Complex Engineering Service Systemsming at this problem, we present a fast method based on climbing algorithms to locate the information source with less calculation cost in large scale social networks. Experimental results on both generated and real-world data sets show that our algorithm is more faster than existing algorithms, since it needs fewer iterations.
36#
發(fā)表于 2025-3-27 19:55:01 | 只看該作者
37#
發(fā)表于 2025-3-27 23:45:56 | 只看該作者
38#
發(fā)表于 2025-3-28 06:05:54 | 只看該作者
Conference proceedings 2015e following topics: mathematical issues in data science; big data issues andapplications; data quality and data preparation; data-driven scientificresearch; evaluation and measurement in data service; big data mining andknowledge management; case study of data science; social impacts of datascience..
39#
發(fā)表于 2025-3-28 07:51:53 | 只看該作者
40#
發(fā)表于 2025-3-28 14:20:22 | 只看該作者
Anna Lukasiewicz,Tayanah O’Donnelld KL divergence to analysis the influence and relevance from a topic modeling perspective. Then two topic-aware heuristics based on betweenness and out-degree for finding approximate solutions to this problem are proposed. Using two real networks, we demonstrate experimentally the high performance of the proposed models and learning schemes.
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