research-article

Adaptive p-posterior mixture-model kernels for multiple instance learning

Authors:

Hongbin ZhaAuthors Info & Claims

ICML '08: Proceedings of the 25th international conference on Machine learning

Pages 1136 - 1143

https://doi.org/10.1145/1390156.1390299

Published: 05 July 2008 Publication History

Abstract

In multiple instance learning (MIL), how the instances determine the bag-labels is an essential issue, both algorithmically and intrinsically. In this paper, we show that the mechanism of how the instances determine the bag-labels is different for different application domains, and does not necessarily obey the traditional assumptions of MIL. We therefore propose an adaptive framework for MIL that adapts to different application domains by learning the domain-specific mechanisms merely from labeled bags. Our approach is especially attractive when we are encountered with novel application domains, for which the mechanisms may be different and unknown. Specifically, we exploit mixture models to represent the composition of each bag and an adaptable kernel function to represent the relationship between the bags. We validate on synthetic MIL datasets that the kernel function automatically adapts to different mechanisms of how the instances determine the bag-labels. We also compare our approach with state-of-the-art MIL techniques on real-world benchmark datasets.

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Cited By

Wen CLi ZLi AQu J(2019)Deep Feature Fusion Multiple Instance Learning for WaDang RecognitionIEEE Access10.1109/ACCESS.2019.29298377(98555-98564)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2929837
Zhou LPlant CBohm C(2017)Joint Gaussian Based Measures for Multiple-Instance Learning2017 IEEE 33rd International Conference on Data Engineering (ICDE)10.1109/ICDE.2017.75(203-206)Online publication date: Apr-2017
https://doi.org/10.1109/ICDE.2017.75
Xu TGondra IChiu D(2017)A maximum partial entropy-based method for multiple-instance concept learningApplied Intelligence10.1007/s10489-016-0873-046:4(865-875)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10489-016-0873-0
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Index Terms

Adaptive p-posterior mixture-model kernels for multiple instance learning
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. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
      2. Modeling methodologies

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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

General Chair:
William Cohen
Carnegie Mellon University
,
Program Chairs:
Andrew McCallum
University of Massachusetts Amherst
,
Sam Roweis
University of Toronto and Google

Copyright © 2008 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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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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ICML '08

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Microsoft Research
Intel
IBM

ICML '08: The 25th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

July 5 - 9, 2008

Helsinki, Finland

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Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

Wen CLi ZLi AQu J(2019)Deep Feature Fusion Multiple Instance Learning for WaDang RecognitionIEEE Access10.1109/ACCESS.2019.29298377(98555-98564)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2929837
Zhou LPlant CBohm C(2017)Joint Gaussian Based Measures for Multiple-Instance Learning2017 IEEE 33rd International Conference on Data Engineering (ICDE)10.1109/ICDE.2017.75(203-206)Online publication date: Apr-2017
https://doi.org/10.1109/ICDE.2017.75
Xu TGondra IChiu D(2017)A maximum partial entropy-based method for multiple-instance concept learningApplied Intelligence10.1007/s10489-016-0873-046:4(865-875)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10489-016-0873-0
Zhou LYe WPlant CBöhm C(2017)Knowledge Discovery of Complex Data Using Gaussian Mixture ModelsBig Data Analytics and Knowledge Discovery10.1007/978-3-319-64283-3_30(409-423)Online publication date: 3-Aug-2017
https://doi.org/10.1007/978-3-319-64283-3_30
Tomar DAgarwal S(2016)Twin Support Vector Machine for Multiple Instance Learning Based on Bag DissimilaritiesAdvances in Artificial Intelligence10.1155/2016/12697082016(1)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1155/2016/1269708
Hu WDing XLi BWang JGao YWang FMaybank S(2016)Multi-Perspective Cost-Sensitive Context-Aware Multi-Instance Sparse Coding and Its Application to Sensitive Video RecognitionIEEE Transactions on Multimedia10.1109/TMM.2015.249637218:1(76-89)Online publication date: Jan-2016
https://doi.org/10.1109/TMM.2015.2496372
Wang XWei DCheng HFang J(2016)Multi-instance learning based on representative instance and feature mappingNeurocomputing10.1016/j.neucom.2016.07.055216:C(790-796)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.07.055
Tax DCheplygina VDuin Rvan de Poll J(2016)The Similarity Between DissimilaritiesStructural, Syntactic, and Statistical Pattern Recognition10.1007/978-3-319-49055-7_8(84-94)Online publication date: 5-Nov-2016
https://doi.org/10.1007/978-3-319-49055-7_8
Herrera FVentura SBello RCornelis CZafra ASánchez-Tarragó DVluymans SHerrera FVentura SBello RCornelis CZafra ASánchez-Tarragó DVluymans S(2016)Multi-instance ClassificationMultiple Instance Learning10.1007/978-3-319-47759-6_3(35-66)Online publication date: 9-Nov-2016
https://doi.org/10.1007/978-3-319-47759-6_3
Wang XZhu ZYao CBai X(2015)Relaxed Multiple-Instance SVM with Application to Object DiscoveryProceedings of the 2015 IEEE International Conference on Computer Vision (ICCV)10.1109/ICCV.2015.145(1224-1232)Online publication date: 7-Dec-2015
https://dl.acm.org/doi/10.1109/ICCV.2015.145
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