poster

Active learning in parallel universes

Authors:

Nicolas Cebron,

Michael R. BertholdAuthors Info & Claims

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Pages 1621 - 1624

https://doi.org/10.1145/1871437.1871688

Published: 26 October 2010 Publication History

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Abstract

This work addresses two challenges in combination: learning with a very limited number of labeled training examples (active learning) and learning in the presence of multiple views for each object where the global model to be learned is spread out over some or all of these views (learning in parallel universes). We propose a new active learning approach which selects the best samples to query the label with the goal of improving overall model accuracy and determining which universe contributes most to the local model. The resulting combination and class-specific weighting of universes provides a significantly better classification accuracy than traditional active learning methods.

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Cited By

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Ranganathan HVenkateswara HChakraborty SPanchanathan S(2017)Deep active learning for image classification2017 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2017.8297020(3934-3938)Online publication date: Sep-2017
https://doi.org/10.1109/ICIP.2017.8297020
Li LJin XPan SSun JYang QAgarwal DPei J(2012)Multi-domain active learning for text classificationProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2339530.2339701(1086-1094)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2339530.2339701

Index Terms

Active learning in parallel universes
1. Information systems
  1. Information systems applications

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Information & Contributors

Information

Published In

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

October 2010

2036 pages

ISBN:9781450300995

DOI:10.1145/1871437

General Chair:
Jimmy Huang
York University, Canada
,
Program Chairs:
Nick Koudas
University of Toronto, Canada
,
Gareth Jones
Dublin City University, Ireland
,
Xindong Wu
University of Vermont, USA
,
Kevyn Collins-Thompson
Microsoft Research, USA
,
Aijun An
York University, Canada

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2010

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CIKM '10

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CIKM '10: International Conference on Information and Knowledge Management

October 26 - 30, 2010

ON, Toronto, Canada

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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Ranganathan HVenkateswara HChakraborty SPanchanathan S(2017)Deep active learning for image classification2017 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2017.8297020(3934-3938)Online publication date: Sep-2017
https://doi.org/10.1109/ICIP.2017.8297020
Li LJin XPan SSun JYang QAgarwal DPei J(2012)Multi-domain active learning for text classificationProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2339530.2339701(1086-1094)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2339530.2339701

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Multiple-view multiple-learner active learning

Active deep Q-learning with demonstration

On the application of active learning for efficient and effective IoT botnet detection