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Hierarchical sampling for active learning

Published: 05 July 2008 Publication History

Abstract

We present an active learning scheme that exploits cluster structure in data.

References

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Balcan, M.-F., Beygelzimer, A., & Langford, J. (2006). Agnostic active learning. ICML.
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Blei, D., Ng, A., & Jordan, M. (2003). Latent dirichlet allocation. JMLR, 3, 993--1022.
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Castro, R., & Nowak, R. (2007). Minimax bounds for active learning. COLT.
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Dasgupta, S. (2005). Coarse sample complexity bounds for active learning. NIPS.
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Dasgupta, S., Hsu, D., & Monteleoni, C. (2007). A general agnostic active learning algorithm. Neural Information Processing Systems.
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Freund, Y., Seung, H., Shamir, E., & Tishby, N. (1997). Selective sampling using the query by committee algorithm. Machine Learning, 28, 133--168.
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Hanneke, S. (2007). A bound on the label complexity of agnostic active learning. ICML.
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Schohn, G., & Cohn, D. (2000). Less is more: active learning with support vector machines. ICML.
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Schutze, H., Velipasaoglu, E., & Pedersen, J. (2006). Performance thresholding in practical text classification. ACM International Conference on Information and Knowledge Management.

Cited By

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  • (2024)Boosting active domain adaptation with exploration of samplesIntelligent Data Analysis10.3233/IDA-23015028:3(667-683)Online publication date: 28-May-2024
  • (2024)Interactive Machine Teaching by Labeling Rules and InstancesTransactions of the Association for Computational Linguistics10.1162/tacl_a_0070712(1441-1459)Online publication date: 18-Nov-2024
  • (2024)Energy-based Active Learning for Bringing Beam-induced Domain Gap for 3D Object DetectionProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3694723(1986-1991)Online publication date: 4-Dec-2024
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Published In

cover image ACM Other conferences
ICML '08: Proceedings of the 25th international conference on Machine learning
July 2008
1310 pages
ISBN:9781605582054
DOI:10.1145/1390156
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • Pascal
  • University of Helsinki
  • Xerox
  • Federation of Finnish Learned Societies
  • Google Inc.
  • NSF
  • Machine Learning Journal/Springer
  • Microsoft Research: Microsoft Research
  • Intel: Intel
  • Yahoo!
  • Helsinki Institute for Information Technology
  • IBM: IBM

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2008

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  • Research-article

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ICML '08
Sponsor:
  • Microsoft Research
  • Intel
  • IBM

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Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

View all
  • (2024)Boosting active domain adaptation with exploration of samplesIntelligent Data Analysis10.3233/IDA-23015028:3(667-683)Online publication date: 28-May-2024
  • (2024)Interactive Machine Teaching by Labeling Rules and InstancesTransactions of the Association for Computational Linguistics10.1162/tacl_a_0070712(1441-1459)Online publication date: 18-Nov-2024
  • (2024)Energy-based Active Learning for Bringing Beam-induced Domain Gap for 3D Object DetectionProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3694723(1986-1991)Online publication date: 4-Dec-2024
  • (2024)Multikernel similarity‐based clustering of amorphous systems and machine‐learned interatomic potentials by active learningJournal of the American Ceramic Society10.1111/jace.20128108:1Online publication date: 20-Sep-2024
  • (2024)Critical Gap Between Generalization Error and Empirical Error in Active Learning2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00275(2759-2767)Online publication date: 3-Jan-2024
  • (2024)Distribution Matching for Machine TeachingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.325641235:9(12316-12329)Online publication date: Sep-2024
  • (2024)Hierarchical Active Learning With Label Proportions on Data RegionsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.341958836:12(8434-8446)Online publication date: Dec-2024
  • (2024)Using Methods From Dimensionality Reduction for Active Learning With Low Query BudgetIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336518936:8(4317-4330)Online publication date: Aug-2024
  • (2024)Overcoming Overconfidence for Active LearningIEEE Access10.1109/ACCESS.2024.344991512(118707-118716)Online publication date: 2024
  • (2024)Dynamic exploration–exploitation trade-off in active learning regression with Bayesian hierarchical modelingIISE Transactions10.1080/24725854.2024.2332910(1-15)Online publication date: 22-Apr-2024
  • Show More Cited By

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