Titlebook: Advances in Knowledge Discovery and Data Mining; 26th Pacific-Asia Co João Gama,Tianrui Li,Fei Teng Conference proceedings 2022 The Editor(

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书目名称Advances in Knowledge Discovery and Data Mining影响因子(影响力)




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书目名称Advances in Knowledge Discovery and Data Mining网络公开度学科排名




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书目名称Advances in Knowledge Discovery and Data Mining被引频次学科排名




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书目名称Advances in Knowledge Discovery and Data Mining年度引用学科排名




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书目名称Advances in Knowledge Discovery and Data Mining读者反馈学科排名

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Bin Cao,Qinyu Zhang,Jon W. Mark set biases, i.e., label noise and class imbalance. While both learning with noisy labels and class-imbalanced learning have received tremendous attention, existing works mainly focus on one of these two training set biases. To fill the gap, we propose ., which does not require fitting additional pa

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Y.-W. Peter Hong,Wan-Jen Huang,C.-C. Jay Kuoion between quantum entangled systems often surpasses that between classical systems, quantum information processing methods show superiority that classical methods do not possess. In this paper, we study the virtue of entangled systems and propose a novel classification algorithm called Quantum Ent

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https://doi.org/10.1007/978-1-4615-2253-9ng, a paradigm for computing making use of quantum theory. Quantum computing can empower machine learning with theoretical properties allowing to overcome the limitations of classical computing. The translation of classical algorithms into their quantum counter-part is not trivial and hides many dif