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Article

On the relation between multi-instance learning and semi-supervised learning

Authors:

Jun-Ming XuAuthors Info & Claims

ICML '07: Proceedings of the 24th international conference on Machine learning

Pages 1167 - 1174

https://doi.org/10.1145/1273496.1273643

Published: 20 June 2007 Publication History

Abstract

Multi-instance learning and semi-supervised learning are different branches of machine learning. The former attempts to learn from a training set consists of labeled bags each containing many unlabeled instances; the latter tries to exploit abundant unlabeled instances when learning with a small number of labeled examples. In this paper, we establish a bridge between these two branches by showing that multi-instance learning can be viewed as a special case of semi-supervised learning. Based on this recognition, we propose the MissSVM algorithm which addresses multi-instance learning using a special semi-supervised support vector machine. Experiments show that solving multi-instance problems from the view of semi-supervised learning is feasible, and the MissSVM algorithm is competitive with state-of-the-art multi-instance learning algorithms.

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https://doi.org/10.1109/TPAMI.2024.3357814
Waqas MAhmed STahir MWu JQureshi R(2024)Exploring Multiple Instance Learning (MIL): A brief surveyExpert Systems with Applications10.1016/j.eswa.2024.123893250(123893)Online publication date: Sep-2024
https://doi.org/10.1016/j.eswa.2024.123893
Ma YLuo YYang Z(2024)GCN-based MIL: multi-instance learning utilizing structural relationships among instancesSignal, Image and Video Processing10.1007/s11760-024-03254-618:6-7(5549-5561)Online publication date: 20-May-2024
https://doi.org/10.1007/s11760-024-03254-6
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On the relation between multi-instance learning and semi-supervised learning
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Published In

cover image ACM Other conferences

ICML '07: Proceedings of the 24th international conference on Machine learning

June 2007

1233 pages

ISBN:9781595937933

DOI:10.1145/1273496

Editor:
Zoubin Ghahramani
University of Cambridge, United Kingdom

Copyright © 2007 ACM.

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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Published: 20 June 2007

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ICML '07 & ILP '07

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ICML '07 & ILP '07: The 24th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

June 20 - 24, 2007

Oregon, Corvalis, USA

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Cited By

Xie ZLiu YHe HLi MZhou Z(2024)Weakly Supervised AUC Optimization: A Unified Partial AUC ApproachIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.335781446:7(4780-4795)Online publication date: Jul-2024
https://doi.org/10.1109/TPAMI.2024.3357814
Waqas MAhmed STahir MWu JQureshi R(2024)Exploring Multiple Instance Learning (MIL): A brief surveyExpert Systems with Applications10.1016/j.eswa.2024.123893250(123893)Online publication date: Sep-2024
https://doi.org/10.1016/j.eswa.2024.123893
Ma YLuo YYang Z(2024)GCN-based MIL: multi-instance learning utilizing structural relationships among instancesSignal, Image and Video Processing10.1007/s11760-024-03254-618:6-7(5549-5561)Online publication date: 20-May-2024
https://doi.org/10.1007/s11760-024-03254-6
Waqas MTahir MAuthor MAl-Maadeed SBouridane AWu J(2024)Simultaneous instance pooling and bag representation selection approach for multiple-instance learning (MIL) using vision transformerNeural Computing and Applications10.1007/s00521-024-09417-336:12(6659-6680)Online publication date: 16-Feb-2024
https://doi.org/10.1007/s00521-024-09417-3
Raff EHolt JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Reproducibility in multiple instance learningProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666718(13530-13544)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666718
Park HMegahed AYin POng YMahajan PGuo P(2023)Incorporating experts’ judgment into machine learning modelsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120118228:COnline publication date: 15-Oct-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120118
Waqas MTahir MKhan S(2023)Robust bag classification approach for multi-instance learning via subspace fuzzy clusteringExpert Systems with Applications10.1016/j.eswa.2022.119113214(119113)Online publication date: Mar-2023
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Ahmed M(2022)A Novel Hybrid Convolutional Neural Network Approach for the Stomach Intestinal Early Detection Cancer Subtype ClassificationComputational Intelligence and Neuroscience10.1155/2022/73250642022Online publication date: 1-Jan-2022
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Gong CYang JYou JSugiyama M(2022)Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2020.304499744:6(2841-2855)Online publication date: 1-Jun-2022
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