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| Active Learning Voting Ensemble× | Stemmeensemble× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 1992 | 1990s–2004 |
| Ophavsperson≠ | Seung, H. S., Opper, M., & Sompolinsky, H. | Lam & Suen; Kuncheva, L. I. (systematic treatment) |
| Type≠ | Active learning with ensemble voting | Ensemble (combination of multiple classifiers by vote) |
| Oprindelig kilde≠ | 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 ↗ | Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8 |
| Aliasser | Query by Committee, QBC, active ensemble learning, committee-based active learning | majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble |
| Relaterede | 5 | 5 |
| Resumé≠ | 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. | A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted. |
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