图 书 馆
非监督转移分类：应用到文本分类Unsupervised Transfer Classification: Application to Text Categorization |
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| 课程网址: | http://videolectures.net/kdd2010_yang_utlatc/ |
| 主讲教师: | Tianbao Yang |
| 开课单位: | 爱荷华大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We study the problem of building the classification model for a target class in the absence of any labeled training example for that class. To address this difficult learning problem, we extend the idea of transfer learning by assuming that the following side information is available: (i) a collection of labeled examples belonging to other classes in the problem domain, called the auxiliary classes; (ii) the class information including the prior of the target class and the correlation between the target class and the auxiliary classes. Our goal is to construct the classification model for the target class by leveraging the above data and information. We refer to this learning problem as unsupervised transfer classification. Our framework is based on the generalized maximum entropy model that is effective in transferring the label information of the auxiliary classes to the target class. A theoretical analysis shows that under certain assumption, the classification model obtained by the proposed approach converges to the optimal model when it is learned from the labeled examples for the target class. Empirical study on text categorization over four different data sets verifies the effectiveness of the proposed approach. |
| 关 键 词: | 计算机科学; 模式识别; 分级模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-13：邬启凡(课程编辑志愿者) |
| 阅读次数: | 39 |