图 书 馆
多视图主动学习与半监督学习相结合On Multi-View Active Learning and the Combination with Semi-Supervised Learning |
|
| 课程网址: | http://videolectures.net/icml08_zhou_mval/ |
| 主讲教师: | Zhi-Hua Zhou |
| 开课单位: | 南京大学 |
| 开课时间: | 2008-08-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Multi-view learning has become a hot topic during the past few years. In this paper, we first characterize the sample complexity of multi-view active learning. Under the α-expansion assumption, we get an exponential improvement in the sample complexity from usual Õ(1/ε) to Õ(log 1/ε), requiring neither strong assumption on data distribution such as the data is distributed uniformly over the unit sphere in ℜd nor strong assumption on hypothesis class such as linear separators through the origin. We also give an upper bound of the error rate when the α-expansion assumption does not hold. Then, we analyze the combination of multi-view active learning and semi-supervised learning and get a further improvement in the sample complexity. Finally, we study the empirical behavior of the two paradigms, which verifies that the combination of multi-view active learning and semi-supervised learning is efficient. |
| 关 键 词: | 多视图学习; 均匀分布; 线性分隔符 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-05-31：王勇彬(课程编辑志愿者) |
| 阅读次数: | 179 |