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Batch Mode Active Learning based on Multi-set Clustering
2021
IEEE Access
Batch mode active learning, where a batch of samples is simultaneously selected and labeled, is a challenging task. The challenge lies in how to maintain the informativeness and keep the diversity of selected samples concurrently. We propose a novel batch mode active learning that balances the informativeness and representativeness using multi-set clustering. Our method utilizes a sequential active learner to retain the informativeness by providing a ranking of unlabeled samples and
doi:10.1109/access.2021.3053003
fatcat:h5nmakynrrd2bfbgfc55k5bfdy