@article{oai:u-ryukyu.repo.nii.ac.jp:02009974, author = {Iju, Tatsuyuki and Endo, Satoshi and Toma, Naruaki and Akamine, Yuhei}, issue = {1}, journal = {Journal of Robotics, Networking and Artificial Life}, month = {Jun}, note = {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., 論文}, pages = {24--27}, title = {Estimating Age on Twitter Using Self-Training Semi-Supervised SVM}, volume = {3}, year = {2016} }