图 书 馆
PAC-Bayesian分析及其应用PAC-Bayesian Analysis and Its Applications |
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| 课程网址: | http://videolectures.net/ecmlpkdd2012_seldin_laviolette_shawe_tay... |
| 主讲教师: | Yevgeny Seldin, John Shawe-Taylor, François Laviolette |
| 开课单位: | 哥本哈根大学 |
| 开课时间: | 2012-10-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | PAC-Bayesian analysis is a basic and very general tool for data-dependent analysis in machine learning. By now, it has been applied in such diverse areas as supervised learning, unsupervised learning, and reinforcement learning, leading to state-of-the-art algorithms and accompanying generalization bounds. PAC-Bayesian analysis, in a sense, takes the best out of Bayesian methods and PAC learning and puts it together: (1) it provides an easy way to exploit prior knowledge (like Bayesian methods); (2) it provides strict and explicit generalization guarantees (like VC theory); and (3) it is data-dependent and provides an easy and strict way of exploiting benign conditions (like Rademacher complexities). In addition, PAC-Bayesian bounds directly lead to efficient learning algorithms. Thus, it is a key and basic subject for machine learning. While the first papers on PAC-Bayesian analysis were not easy to readsubsequent simplifications made it possible to explain it literally in three slides. We will start with a general introduction to PAC-Bayesian analysis, which should be accessible to an average student, who is familiar with machine learning at the basic level. Then, we will survey multiple forms of PAC-Bayesian bounds and their numerous applications in different fields, including supervised an, d unsupervised learning, finite and continuous domains, and the very recent extension to martingales and reinforcement learning. Some of these applications will be explained in more details, while others will be surveyed at a high level. We will also describe the relations and distinctions between PAC-Bayesian analysis, Bayesian learning, VC theory, and Rademacher complexities. We will discuss the role, value, and shortcomings of frequentist bounds that are inspired by Bayesian analysis. |
| 关 键 词: | 数据简化; 数据依赖分析; PAC-Bayesian边界; 泛化边界; 先验知识; 机器学习 |
| 课程来源: | 视频讲座网公开课 |
| 最后编审: | 2019-05-26：cwx |
| 阅读次数: | 54 |