方法证据记录
Active Learning Gradient Boosting
Active Learning Gradient Boosting combines the powerful predictive accuracy of gradient boosted trees with an active learning loop that selects the most informative unlabeled examples for human annotation. By querying only the instances the model is most uncertain about, the method achieves high accuracy with far fewer labeled examples than passive supervised learning.
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 Gradient Boosting (Query-by-Committee / Uncertainty Sampling with Gradient Boosted Trees)
分类方法记录 · ml-model / machine-learning
- Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison. · URL
- Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. · DOI 10.1214/aos/1013203451
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