Estimating Age on Twitter Using Self-Training Semi-Supervised SVM

被引：0

作者：

Iju, Tatsuyuki ^{[1
]}

Endo, Satoshi ^{[2
]}

Yamada, Koji ^{[2
]}

Toma, Naruaki ^{[2
]}

Akamine, Yuhei ^{[2
]}

机构：

[1] Univ Ryukyus, Grad Sch Informat Engn, 1 Senbaru, Nishihara, Okinawa, Japan

[2] Univ Ryukyus, Sch Informat Engn, 1 Senbaru, Nishihara, Okinawa, Japan

来源：

PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS (ICAROB 2016) | 2016年

关键词：

Twitter; Age; Semi-supervised learning; Self-training; SVM; Plat scaling;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

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页码：228 / 231

页数：4

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