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Combining labeled and unlabeled data with co-training

Authors:

Avrim Blum,

Tom MitchellAuthors Info & Claims

COLT' 98: Proceedings of the eleventh annual conference on Computational learning theory

Pages 92 - 100

https://doi.org/10.1145/279943.279962

Published: 24 July 1998 Publication History

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Index Terms

Combining labeled and unlabeled data with co-training
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Mathematics of computing
  1. Discrete mathematics

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Information

Published In

COLT' 98: Proceedings of the eleventh annual conference on Computational learning theory

July 1998

304 pages

ISBN:1581130570

DOI:10.1145/279943

Chairmen:
Peter Bartlett
Australian National Univ., Australia
,
Yishay Mansour
Tel-Aviv Univ., Israel

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 1998

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COLT98

Sponsor:

University of Wisconsin
SIGAI
SIGACT
UC @ Santa Cruz

COLT98: The 11th Annual Conference on Computational Learning Theroy

July 24 - 26, 1998

Wisconsin, Madison, USA

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Overall Acceptance Rate 35 of 71 submissions, 49%

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Zhang KQian BZhang XZheng Q(2025)Using cross-domain knowledge augmentation to explore comorbidity in electronic health records dataExpert Systems with Applications10.1016/j.eswa.2024.125644263(125644)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125644
Hongwei ZYuhan CPingping LQiuzhan Z(2024)Medical image segmentation method based on confidence learning and collaborative trainingScientific Insights and Discoveries Review10.59782/sidr.v2i1.1172:1(211-219)Online publication date: 7-Oct-2024
https://doi.org/10.59782/sidr.v2i1.117
Thuong VTran DTran DThu BTung VPhuong NKhanh PTung PVi M(2024)Effective Human Activity Recognition through Accelerometer DataEngineering, Technology & Applied Science Research10.48084/etasr.821114:5(16499-16510)Online publication date: 9-Oct-2024
https://doi.org/10.48084/etasr.8211
Zhao LZhang ZZhu JWang HXie Z(2024)Collaborative Multiple Players to Address Label Sparsity in Quality Prediction of Batch ProcessesSensors10.3390/s2407207324:7(2073)Online publication date: 24-Mar-2024
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Zhang MGu XQi JZhang ZYang HXu JPeng CLi H(2024)CDEST: Class Distinguishability-Enhanced Self-Training Method for Adopting Pre-Trained Models to Downstream Remote Sensing Image Semantic SegmentationRemote Sensing10.3390/rs1607129316:7(1293)Online publication date: 6-Apr-2024
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Shakya KAlavi APorteous JK PLaddi AJaiswal M(2024)A Critical Analysis of Deep Semi-Supervised Learning Approaches for Enhanced Medical Image ClassificationInformation10.3390/info1505024615:5(246)Online publication date: 24-Apr-2024
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