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pool-based active learning

Description: Pool-based active learning is a machine-learning approach that selects informative examples from a fixed pool for labeling to improve a model.
OpenAlex ID: keywords/pool-based-active-learning
10.1162/153244302760185243
2000 · Applied Physics Letters · 850
Variational Adversarial Active Learning
2019 · Samrath Sinha, Sayna Ebrahimi, et al. · IEEE/CVF International Conference on Computer Vision (ICCV) · 585
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A bound on the label complexity of agnostic active learning
2007 · Steve Hanneke · International Conference on Machine Learning · 231
Online Choice of Active Learning Algorithms
2003 · Yoram Baram, Ran El‐Yaniv, et al. · 211
Active Learning Query Strategies for Classification, Regression, and Clustering: A Survey
2020 · Punit Kumar, Atul Gupta · Journal of Computer Science and Technology · 195
Consistency-Based Semi-supervised Active Learning: Towards Minimizing Labeling Cost
2020 · Mingfei Gao, Zizhao Zhang, et al. · Lecture notes in computer science · 180
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Active Learning by Learning
2015 · Wei-Ning Hsu, Hsuan-Tien Lin · Proceedings of the AAAI Conference on Artificial Intelligence · 169
Pool-Based Sequential Active Learning for Regression
2018 · Dongrui Wu · IEEE Transactions on Neural Networks and Learning Systems · 148
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Sequential Graph Convolutional Network for Active Learning
2021 · Razvan Caramalau, Binod Bhattarai, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 131
Active semi-supervised learning using sampling theory for graph signals
2014 · Akshay Gadde, Aamir Anis, et al. · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · 131
Generative Adversarial Active Learning
2017 · Zhu, Jia-Jie, Bento, José · arXiv (Cornell University) · 129
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Active learning for data streams: a survey
2023 · Davide Cacciarelli, Murat Kulahci · Machine Learning · 111
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Active k-labelsets ensemble for multi-label classification
2020 · Ran Wang, Sam Kwong, et al. · Pattern Recognition · 90
Robust Selective Sampling from Single and Multiple Teachers.
2010 · Ofer Dekel, Claudio Gentile, et al. · 90
Pool-based active learning in approximate linear regression
2009 · Masashi Sugiyama, Shinichi Nakajima · Machine Learning · 90
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Deep Active Learning over the Long Tail
2017 · Geifman, Yonatan, El-Yaniv, Ran · arXiv (Cornell University) · 86
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Ranked batch-mode active learning
2016 · Thiago N.C. Cardoso, Rodrigo M. Silva, et al. · Information Sciences · 74
Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments
2016 · Wenjing Han, Eduardo Coutinho, et al. · PLoS ONE · 63
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Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning
2010 · Prateek Jain, Sudheendra Vijayanarasimhan, et al. · 62
Teaching Dimension and the Complexity of Active Learning
2007 · Steve Hanneke · Springer eBooks · 61
A Comparative Survey: Benchmarking for Pool-based Active Learning
2021 · Xueying Zhan, Huan Liu, et al. · 57
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An LDA-SVM Active Learning Framework for Web Service Classification
2016 · Xumin Liu, Shaleen Agarwal, et al. · IEEE International Conference on Web Services (ICWS) · 54
A Load Identification Method Based on Active Deep Learning and Discrete Wavelet Transform
2020 · Luyang Guo, Shouxiang Wang, et al. · IEEE Access · 52
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Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning
2020 · Daniel Grießhaber, Johannes Maucher, et al. · International Conference on Computational Linguistics · 48
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Transductive active learning – A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data
2014 · Tobias Reitmaier, Adrian Calma, et al. · Information Sciences · 45
Works count: 276
Citation count: 5,243
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