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基于无向贝叶斯传递层次的凸点估计Convex Point Estimation using Undirected Bayesian Transfer Hierarchies |
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| 课程网址: | http://videolectures.net/uai08_packer_cpe/ |
| 主讲教师: | Ben Packer |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | When related learning tasks are naturally arranged in a hierarchy, an appealing approach for coping with scarcity of instances is that of transfer learning using a hierarchical Bayes framework. As fully Bayesian computations can be difficult and computationally demanding, it is often desirable to use posterior point estimates that facilitate (relatively) efficient prediction. However, the hierarchical Bayes framework does not always lend itself naturally to this maximum a posteriori goal. In this work we propose an undirected reformulation of hierarchical Bayes that relies on priors in the form of similarity measures. We introduce the notion of “degree of transfer” weights on components of these similarity measures, and show how they can be automatically learned within a joint probabilistic framework. Importantly, our reformulation results in a convex objective for many learning problems, thus facilitating optimal posterior point estimation using standard optimization techniques. In addition, we no longer require proper priors, allowing for flexible and straightforward specification of joint distributions over transfer hierarchies. We show that our framework is effective for learning models that are part of transfer hierarchies for two real-life tasks: object shape modeling using Gaussian density estimation and document classification. |
| 关 键 词: | 计算机科学; 贝叶斯; 算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-17：cwx |
| 阅读次数: | 34 |