Zhen Xu1,2, Weibin Chen1,2,*
Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2627-2650, 2023, DOI:10.32604/iasc.2023.040497
Abstract Active learning (AL) trains a high-precision predictor model from small numbers of labeled data by iteratively annotating the most valuable data sample from an unlabeled data pool with a class label throughout the learning process. However, most current AL methods start with the premise that the labels queried at AL rounds must be free of ambiguity, which may be unrealistic in some real-world applications where only a set of candidate labels can be obtained for selected data. Besides, most of the existing AL algorithms only consider the case of centralized processing, which necessitates gathering together… More >
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