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pro vyhledávání: '"Liu, Qunfeng"'
Performance analysis is crucial in optimization research, especially when addressing black-box problems through nature-inspired algorithms. Current practices often rely heavily on statistical methods, which can lead to various logical paradoxes. To a
Externí odkaz:
http://arxiv.org/abs/2410.21677
Autor:
Jinng, Yunpeng, Liu, Qunfeng
Evaluating performance across optimization algorithms on many problems presents a complex challenge due to the diversity of numerical scales involved. Traditional data processing methods, such as hypothesis testing and Bayesian inference, often emplo
Externí odkaz:
http://arxiv.org/abs/2409.04479
The key to the text classification task is language representation and important information extraction, and there are many related studies. In recent years, the research on graph neural network (GNN) in text classification has gradually emerged and
Externí odkaz:
http://arxiv.org/abs/2209.07031
Publikováno v:
In Swarm and Evolutionary Computation December 2024 91
Publikováno v:
In Structures August 2024 66
Publikováno v:
In Information Sciences March 2024 660
Publikováno v:
In Information Sciences May 2023 627:169-188
Autor:
Nie, Huihui, Xu, Xiong, Liu, Qunfeng, Chen, Hongsheng, Zheng, Liuwei, Kong, Qingwei, Liang, Wei
Publikováno v:
In Materials Science & Engineering A 26 April 2023 871
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Image similarity measures play an important role in nearest neighbor search and duplicate detection for large-scale image datasets. Recently, Minwise Hashing (or Minhash) and its related hashing algorithms have achieved great performances in large-sc
Externí odkaz:
http://arxiv.org/abs/1807.02895