Real: A representative error-driven approach for active learning

Real: A representative error-driven approach for active learning

Given a limited labeling budget, active learning (al) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training. To achieve this, al typically measures the informativeness of unlabeled instances based on uncertainty and diversity. However, i...

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書目詳細資料
Main Authors:	CHEN, Cheng, WANG, Yong, LIAO, Lizi, CHEN, Yueguo, DU, Xiaoyong
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2023
主題:	Active Learning Error density Error-driven Informativeness Labelings Model training Neighbourhood Pseudo errors Text classification Databases and Information Systems
在線閱讀:	https://ink.library.smu.edu.sg/sis_research/8586 https://ink.library.smu.edu.sg/context/sis_research/article/9589/viewcontent/real.pdf
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Singapore Management University
語言:	English

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