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作者:

Yang, Ruiqi (Yang, Ruiqi.) | Xu, Dachuan (Xu, Dachuan.) (学者:徐大川) | Guo, Longkun (Guo, Longkun.) | Zhang, Dongmei (Zhang, Dongmei.)

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EI

摘要:

We study the submodular maximization problem in generalized streaming setting using a two-stage policy. In the streaming context, elements are released in a fashion that an element is revealed at one time. Subject to a limited memory capacity, the problem aims to sieve a subset of elements with a sublinear size, such that the expecting objective value of all utility functions over the summarized subsets has a performance guarantee. We present a generalized one pass, -approximation, which consumes memory and runs in time, where k, n, m and denote the cardinality constraint, the element stream size, the amount of the learned functions, and the minimum generic submodular ratio of the learned functions, respectively. © 2020, Springer Nature Switzerland AG.

关键词:

Artificial intelligence Computation theory Computers Computer science

作者机构:

  • [ 1 ] [Yang, Ruiqi]Department of Operations Research and Scientific Computing, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Xu, Dachuan]Department of Operations Research and Scientific Computing, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Guo, Longkun]School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan; 250353, China
  • [ 4 ] [Zhang, Dongmei]School of Computer Science and Technology, Shandong Jianzhu University, Jinan; 250101, China

通讯作者信息:

  • [zhang, dongmei]school of computer science and technology, shandong jianzhu university, jinan; 250101, china

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ISSN: 0302-9743

年份: 2020

卷: 12337 LNCS

页码: 193-204

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 2

ESI高被引论文在榜: 0 展开所有

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