| Title: | GS2-3 Estimating Age on Twitter Using Self-Training Semi-Supervised SVM |
|---|---|
| Publication: | ICAROB2016 |
| Volume: | 21 |
| Pages: | 228-231 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2016.GS2-3 |
| Author(s): | Tatsuyuki Iju, Satoshi Endo, Koji Yamada, Naruaki Toma, Yuhei Akamine |
| Publication Date: | January 29, 2016 |
| 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. |
| PDF File: | https://alife-robotics.co.jp/members2016/icarob/data/papers/GS/GS2-3.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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