research-article

Activity recognition with the aid of unlabeled samples

Authors:

Sungyoung LeeAuthors Info & Claims

ICUIMC '09: Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication

Pages 670 - 674

https://doi.org/10.1145/1516241.1516359

Published: 15 February 2009 Publication History

Abstract

Activity recognition is an important topic in ubiquitous computing. In activity recognition, supervised learning techniques have been widely applied to learn the activity models. However, most of them can only utilize labeled samples for learning even though a large amount of unlabeled samples exist. In our previous work, we have proposed a semi-supervised learning method which can utilize both labeled and unlabeled samples for learning. As an alternative, a new learning method is proposed in this work. It makes use of the unlabeled samples to remove the noises from labeled samples, so that the learning performance is improved. Experimental results show the effectiveness of our method.

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Cited By

Amft O(2011)Self-Taught Learning for Activity Spotting in On-body Motion Sensor DataProceedings of the 2011 15th Annual International Symposium on Wearable Computers10.1109/ISWC.2011.37(83-86)Online publication date: 12-Jun-2011
https://dl.acm.org/doi/10.1109/ISWC.2011.37
Amft O(2010)Adaptive Activity Spotting Based on Event RatesProceedings of the 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing10.1109/SUTC.2010.63(169-176)Online publication date: 7-Jun-2010
https://dl.acm.org/doi/10.1109/SUTC.2010.63
Ma TGe JYan QGuan D(2010)Enlarge the Training Data for Activity RecognitionProceedings of the 2010 Third International Conference on Information and Computing - Volume 0410.1109/ICIC.2010.354(329-332)Online publication date: 4-Jun-2010
https://dl.acm.org/doi/10.1109/ICIC.2010.354
Show More Cited By

Index Terms

Activity recognition with the aid of unlabeled samples
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

ICUIMC '09: Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication

February 2009

704 pages

ISBN:9781605584058

DOI:10.1145/1516241

General Chairs:
Won Kim
Sungkyunkwan University, Korea
,
Hyung Jin Choi
Sungkyunkwan University, Korea
,
Dongho Won
SungKyunkwan University, Korea

Copyright © 2009 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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SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 February 2009

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Ministry of Knowledge Economy

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ICUIMC '09

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SIGKDD

ICUIMC '09: The 3rd International Conference on Ubiquitous Information Management and Communication

January 15 - 16, 2009

Suwon, Korea

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Overall Acceptance Rate 251 of 941 submissions, 27%

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Cited By

Amft O(2011)Self-Taught Learning for Activity Spotting in On-body Motion Sensor DataProceedings of the 2011 15th Annual International Symposium on Wearable Computers10.1109/ISWC.2011.37(83-86)Online publication date: 12-Jun-2011
https://dl.acm.org/doi/10.1109/ISWC.2011.37
Amft O(2010)Adaptive Activity Spotting Based on Event RatesProceedings of the 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing10.1109/SUTC.2010.63(169-176)Online publication date: 7-Jun-2010
https://dl.acm.org/doi/10.1109/SUTC.2010.63
Ma TGe JYan QGuan D(2010)Enlarge the Training Data for Activity RecognitionProceedings of the 2010 Third International Conference on Information and Computing - Volume 0410.1109/ICIC.2010.354(329-332)Online publication date: 4-Jun-2010
https://dl.acm.org/doi/10.1109/ICIC.2010.354
Siddiqi MFahim MLee SLee Y(2010)Human activity recognition based on morphological dilation followed by watershed transformation method2010 International Conference on Electronics and Information Engineering10.1109/ICEIE.2010.5559811(V2-433-V2-437)Online publication date: Aug-2010
https://doi.org/10.1109/ICEIE.2010.5559811

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