图 书 馆
Priors样本大小的可识别性以及转移学习的应用Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning |
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| 课程网址: | http://videolectures.net/colt2011_hanneke_transfer/ |
| 主讲教师: | Steve Hanneke |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2011-08-02 |
| 课程语种: | 英语 |
| 中文简介: |  我们探索了一种转移学习设置，其中有限的目标概念序列是独立采样的，具有来自已知家族的未知分布。我们研究了将所有目标学习到任意指定的预期精度所需的标记示例总数，重点关注任务数量和所需精度的渐近性。我们的主要兴趣是正式理解转学习的基本益处，而不是独立学习每个目标。我们对转移问题的处理方法是通用的，因为它可以与各种学习协议一起使用。推动我们的方法的关键洞察力是目标概念的分布可以通过与概念空间的Vapnik Chervonenkis维度相等的多个随机标记数据点的联合分布来识别。对于任何较少数量的点的联合分布，情况不一定如此。当应用于主动学习方法时，这项工作具有特别有意义的含义。 |
| 课程简介: | We explore a transfer learning setting, in which a finite sequence of target concepts are sampled independently with an unknown distribution from a known family. We study the total number of labeled examples required to learn all targets to an arbitrary specified expected accuracy, focusing on the asymptotics in the number of tasks and the desired accuracy. Our primary interest is formally understanding the fundamental benefits of transfer learning, compared to learning each target independently from the others. Our approach to the transfer problem is general, in the sense that it can be used with a variety of learning protocols. The key insight driving our approach is that the distribution of the target concepts is identifiable from the joint distribution over a number of random labeled data points equal the Vapnik-Chervonenkis dimension of the concept space. This is not necessarily the case for the joint distribution over any smaller number of points. This work has particularly interesting implications when applied to active learning methods. |
| 关 键 词: | 转移学习; 概念序列; 洞察力 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 50 |