A survey on instance selection for active learning

By Yifan Fu, Xingquan Zhu, Bin Li. 2012

394 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Understanding Dataset Difficulty with V-Usable Information (2021, neutral): “Estimating instance difficulty is also evocative of instance selection for active learning (Lewis & Catlett, 1994; Fu et al., 2013; Liu & Motoda, 2002); however these estimates could change as the dataset picks up new instances.”
Local Rule-Based Explanations of Black Box Decision Systems (2018, methodology): “train b is available, or not even that b is a supervised machine learning predictor for which a training set exists. Our task is instead similar to instance generation in the field of active learning [14], also including evolutionary approaches [10]. We adopt an approach based on a geneticalgorithmw”
FITAnnotator: A Flexible and Intelligent Text Annotation System (2021, methodology): “In this experiment, we respectively explore the effectiveness of the uncertainty sampling and the diversity sampling in active learning (Fu et al., 2013), and employ a random sampling strategy as the baseline method.”
When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018, methodology): “ in geostatistics [7], and surrogates or emulators in computer experiments [8]. The GP is a nonparametric statistical model which has been extensively used in various scenarios, e.g., active learning [9], [10], multitask learning [11], [12], manifold learning [13], reinforcement learning [14], and o”
A Survey of Human-in-the-loop for Machine Learning (2021, background): “There are multiple mature and worthy reference methods in selecting criteria, and perhaps researchers can obtain inspiration from these methods [147].”
Manipulating Attributes of Natural Scenes via Hallucination (2018, background): “From training point of view, a careful selection of “negative” pairs, i.e. negative mining, is an essential component in metric learning and ranking [Fu et al. 2013; Li et al. 2013; Shrivastava et al. 2016].”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, background): “In the field of machine learning, Settles (2010) and Fu et al. (2013) reviewed the active learning approaches mainly for classification problems.”
Information-Theoretic Measures of Dataset Difficulty (2021, background): “Estimating instance difﬁculty is evocative of instance selection for active learning (Fu et al., 2013; Liu and Motoda, 2002).”