Authors
Tatsuyuki Iju, Satoshi Endo, Koji Yamada, Naruaki Toma, Yuhei Akamine
Corresponding Author
Tatsuyuki Iju
Available Online 1 June 2016.
DOI
https://doi.org/10.2991/jrnal.2016.3.1.6How to use a DOI?
Keywords
Twitter, Age, Semi-supervised learning, Self-training, SVM, Plat scaling
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.

Copyright
© 2013, the Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).