Presentation 2014/7/25
Active Learning Based on Geographical Orientation for Automatic Transportation Mode Estimation
BRENDAN COWAN, YOSHIHIKO SUHARA, HIROYUKI TODA, YOSHIMASA KOIKE,
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Abstract(in English) We focus on the automatic transportation estimation task, which automatically estimates transportation modes given GPS trajectories of a user. Previous works have used supervised learning frameworks to estimate transportation modes and have reported that it achieves certain performances. However, the main drawback of supervised learning is the requirement of a significant amount of labeled data. Active learning is an effective solution to this problem. Although many studies have developed a wide variety of active learning algorithms, it has previously been unclear as to whether active learning works well for the automatic transportation mode estimation task. In addition, no previous work reveals which aspects are useful for selecting better instances in terms of model improvement for this task. We propose a novel aspect, geographical orientation, to develop a semi-stream-based active learning method. Our method takes into account geographical distance and density separately from the information based solely on feature space.
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Paper # Vol.2014-DBS-159 No.4,Vol.2014-IFAT-115 No.4
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Committee DE
Conference Date 2014/7/25(1days)
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Language ENG
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Title (in English) Active Learning Based on Geographical Orientation for Automatic Transportation Mode Estimation
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1st Author's Name BRENDAN COWAN
1st Author's Affiliation University of Alberta()
2nd Author's Name YOSHIHIKO SUHARA
2nd Author's Affiliation NTT Service Evolution Laboratories, NTT Corporation
3rd Author's Name HIROYUKI TODA
3rd Author's Affiliation NTT Service Evolution Laboratories, NTT Corporation
4th Author's Name YOSHIMASA KOIKE
4th Author's Affiliation NTT Service Evolution Laboratories, NTT Corporation
Date 2014/7/25
Paper # Vol.2014-DBS-159 No.4,Vol.2014-IFAT-115 No.4
Volume (vol) vol.114
Number (no) 173
Page pp.pp.-
#Pages 6
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