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Title :Estimating Age on Twitter Using Self-Training Semi-Supervised SVM
Authors :Iju, Tatsuyuki
Endo, Satoshi
Toma, Naruaki
Akamine, Yuhei
Issue Date :Jun-2016
Abstract :The estimation methods for Twitter user’s attributes typically require a vast amount of labeled data. Therefore, an efficient way is to tag the unlabeled data and add it to the set. We applied the self-training SVM as a semi-supervised method for age estimation and introduced Plat scaling as the unlabeled data selection criterion in the self-training process. We show how the performance of the self-training SVM varies when the amount of training data and the selection criterion values are changed.
URL :http://dx.doi.org/10.2991/jrnal.2016.3.1.6
Type Local :雑誌掲載論文
ISSN :2352-6386
Publisher :Atlantis Press
URI :http://hdl.handle.net/20.500.12000/42185
Citation :Journal of Robotics, Networking and Artificial Life Vol.3 no.1 p.24 -27
Appears in Collections:Peer-reviewed Journal Articles (Faculty of Engineering)

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