图 书 馆
MedLDA：回归和分类的最大保证金监督主题模型MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification |
|
| 课程网址: | http://videolectures.net/icml09_zhu_mlda/ |
| 主讲教师: | Jun Zhu |
| 开课单位: | 清华大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Supervised topic models utilize document’s side information for discovering predictive low dimensional representations of documents; and existing models apply likelihood-based estimation. In this paper, we present a max-margin supervised topic model for both continuous and categorical response variables. Our approach, the maximum entropy discrimination latent Dirichlet allocation (MedLDA), utilizes the max-margin principle to train supervised topic models and estimate predictive topic representations that are arguably more suitable for prediction. We develop efficient variational methods for posterior inference and demonstrate qualitatively and quantitatively the advantages of MedLDA over likelihood-based topic models on movie review and 20 Newsgroups data sets. |
| 关 键 词: | 监督主题模型; 预测低维; 似然 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-25：cwx |
| 阅读次数: | 127 |