方法证据记录
Active Learning Voting Ensemble
Active Learning Voting Ensemble — formally known as Query by Committee — is an active learning strategy that trains a committee of diverse models and selects the unlabeled examples where the committee members disagree most for human annotation. By focusing labeling effort on the most informative points, it achieves high accuracy with far fewer labeled examples than passive learning requires.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Active Learning with Voting Ensemble (Query by Committee)
分类方法记录 · ml-model / machine-learning
- Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (COLT '92), pp. 287–294. ACM. · DOI 10.1145/130385.130417
- Settles, B. (2009). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison. · URL
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