图 书 馆
多视图混合估计模型的有效算法Efficient algorithms for estimating multi-view mixture models |
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| 课程网址: | http://videolectures.net/nipsworkshops2012_hsu_algorithms/ |
| 主讲教师: | Daniel Hsu |
| 开课单位: | 微软公司 |
| 开课时间: | 2013-01-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Mixture models are a staple in machine learning and applied statistics for treating data taken from multiple sub-populations. For many classes of mixture models, parameter estimation is computationally and/or information-theoretically hard in general. However, much progress has been made over the past decade or so to overcome these hardness barriers by focusing on sub-classes that rule out the intractable cases. One very powerful and general sub-class is the multi-view setting, where one can take advantage of several non-redundant sources of information to help distinguish different sub-populations. In this talk, I'll describe a general technique that is applicable even in semi-parametric settings, where one may not have a parametric model for individual mixture components. This technique also yields a number of new unsupervised learning results for well-studied problems, as well as very practical and scalable learning algorithms. |
| 关 键 词: | 矩量法; 多视图混合模型; 马尔可夫模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 33 |