Article

Margin-Based active learning for structured output spaces

Authors:

Kevin SmallAuthors Info & Claims

ECML'06: Proceedings of the 17th European conference on Machine Learning

Pages 413 - 424

https://doi.org/10.1007/11871842_40

Published: 18 September 2006 Publication History

Abstract

In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to improve the global performance. Typically, these structured output scenarios are also characterized by a high cost associated with obtaining supervised training data, motivating the study of active learning for these situations. Starting with active learning approaches for multiclass classification, we first design querying functions for selecting entire structured instances, exploring the tradeoff between selecting instances based on a global margin or a combination of the margin of local classifiers. We then look at the setting where subcomponents of the structured instance can be queried independently and examine the benefit of incorporating structural information in such scenarios. Empirical results on both synthetic data and the semantic role labeling task demonstrate a significant reduction in the need for supervised training data when using the proposed methods.

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Cited By

Tae KZhang HPark JRong KWhang S(2024)Falcon: Fair Active Learning Using Multi-Armed BanditsProceedings of the VLDB Endowment10.14778/3641204.364120717:5(952-965)Online publication date: 1-Jan-2024
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Han FYe PDuan SWang LCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Ada-iD: Active Domain Adaptation for Intrusion DetectionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681272(7404-7413)Online publication date: 28-Oct-2024
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Huang ZChen ZLi YDong BZhou ELiu YGoh RFeng CZuo WCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Class Balance Matters to Active Class-Incremental LearningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680822(9445-9454)Online publication date: 28-Oct-2024
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Index Terms

Margin-Based active learning for structured output spaces
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

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Information & Contributors

Information

Published In

cover image Guide Proceedings

ECML'06: Proceedings of the 17th European conference on Machine Learning

September 2006

848 pages

ISBN:354045375X

Editors:
Johannes Fürnkranz
Knowledge Engineering Group, Technische Universität Darmstadt
,
Tobias Scheffer
Knowledge Engineering Group, Max Planck Institute for Computer Science, Saarbrücken, Germany
,
Myra Spiliopoulou
Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Saarbrücken, Germany

Sponsors

Pascal
Deutsche Forschungsgemeinschaft
Gobierno de España-Ministerio de Ciencia e InnovaciTn
Strato AG: Strato AG
IBM: IBM

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 18 September 2006

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Cited By

Tae KZhang HPark JRong KWhang S(2024)Falcon: Fair Active Learning Using Multi-Armed BanditsProceedings of the VLDB Endowment10.14778/3641204.364120717:5(952-965)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.14778/3641204.3641207
Han FYe PDuan SWang LCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Ada-iD: Active Domain Adaptation for Intrusion DetectionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681272(7404-7413)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681272
Huang ZChen ZLi YDong BZhou ELiu YGoh RFeng CZuo WCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Class Balance Matters to Active Class-Incremental LearningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680822(9445-9454)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680822
Yang JMa SZhang ZLi YXiao SWen JLu WGao X(2024)Say No to Redundant Information: Unsupervised Redundant Feature Elimination for Active LearningIEEE Transactions on Multimedia10.1109/TMM.2024.337119226(7721-7733)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3371192
Wang MWen TJiang XZhang A(2024)Open set transfer learning through distribution driven active learningPattern Recognition10.1016/j.patcog.2023.110055146:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.patcog.2023.110055
Cacciarelli DKulahci M(2024)Active learning for data streams: a surveyMachine Language10.1007/s10994-023-06454-2113:1(185-239)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10994-023-06454-2
Thrasher JAmireskandari AGyawali P(2024)Enhancing Retinal Disease Classification from OCTA Images via Active Learning TechniquesData Engineering in Medical Imaging10.1007/978-3-031-73748-0_14(134-143)Online publication date: 11-Oct-2024
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Park DChoi SKim DSong HLee JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Robust data pruning under label noise via maximizing re-labeling accuracyProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669379(74501-74514)Online publication date: 10-Dec-2023
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Xie BLi SGuo QLiu CCheng XOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)AnnotatorProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668224(48444-48458)Online publication date: 10-Dec-2023
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Saran AYousefi SKrishnamurthy ALangford JAsh JKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Streaming active learning with deep neural networksProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619653(30005-30021)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619653
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