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Updated: Jun 5, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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积极的未标记的学习选择不是随机的 (PULSNAR):在没有选择完全随机的假设的情况下估计类比例

Praveen Kumar1, Christophe G Lambert1

  • 1Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, United States.

PeerJ. Computer science
|December 9, 2024
PubMed
概括

新的算法通过解决选择非随机 (SNAR) 问题来改善积极和未标记 (PU) 学习. PULSNAR在真实世界的场景中提高了分类准确性,真负数很少.

关键词:
阶级不平衡造成的不平衡课堂上级的先行阶级.机器学习 机器学习混合物比例估计的估计.噪音标签学习学习普尔斯卡尔 (PULSCAR) 是一个这是一个PULSNAR.积极的未标记的学习.可能性校准的概率.这是一个可怕的恐惧.这就是SNAR SNAR.半监督学习 半监督学习

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