图 书 馆
学习专业知识建模，当每个人都知道一些东西Modeling annotator expertise: Learning when everybody knows a bit of something |
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| 课程网址: | http://videolectures.net/aistats2010_yan_mae/ |
| 主讲教师: | Yan Yan |
| 开课单位: | 美国东北大学 |
| 开课时间: | 2010-06-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this problem when annotators may be unreliable (labels are noisy), but also their expertise varies depending on the data they observe (annotators may have knowledge about different parts of the input space). That is, an annotator may not be consistently accurate (or inaccurate) across the task domain. The presented approach produces classification and annotator models that allow us to provide estimates of the true labels and annotator variable expertise. We provide an analysis of the proposed model under various scenarios and show experimentally that annotator expertise can indeed vary in real tasks and that the presented approach provides clear advantages over previously introduced multi-annotator methods, which only consider general annotator characteristics. |
| 关 键 词: | 专业建模; 机器学习; 数据挖掘 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-09-20：zyk |
| 阅读次数: | 34 |