Active Learning for Regression Based on Query by Committee

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A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “Query-by-committee can also be extended to regression [110] by training a committee of models and selecting the examples where the variance among the”
Active Learning Literature Survey (2009, background): “, by measuring disagreement as the variance among the committee members’ output predictions (Burbidge et al., 2007).”
Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems (2017, methodology): “be applied to both classification and regression problems [35].”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “For example, Burbidge et al. (2007) used the cross-validation strategy to investigate the performance of QBC for global metamodeling, and pointed that this strategy works when the committee member ’ s bias is small.”
The Power of Ensembles for Active Learning in Image Classification (2018, background): “Active learning concepts relevant for this paper include uncertainty sampling ([42], [32], [35]), query-by-committee ([30], [5]) and density-based approaches ([52], [61], [46]).”
SHIV: Reducing supervisor burden in DAgger using support vectors for efficient learning from demonstrations in high dimensional state spaces (2016, unknown): “randomly selecting which states to query [7].”