图 书 馆
CRF的专业化和潜在的可能性Majorization for CRFs and Latent Likelihoods |
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| 课程网址: | http://videolectures.net/machine_choromanska_majorization/ |
| 主讲教师: | Anna Choromanska |
| 开课单位: | 哥伦比亚大学 |
| 开课时间: | 2013-06-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The partition function plays a key role in probabilistic modeling including conditional random fields, graphical models, and maximum likelihood estimation. To optimize partition functions, this article introduces a quadratic variational upper bound. This inequality facilitates majorization methods: optimization of complicated functions through the iterative solution of simpler sub-problems. Such bounds remain efficient to compute even when the partition function involves a graphical model (with small tree-width) or in latent likelihood settings. For large-scale problems, low-rank versions of the bound are provided and outperform LBFGS as well as first-order methods. Several learning applications are shown and reduce to fast and convergent update rules. Experimental results show advantages over state-of-the-art optimization methods. |
| 关 键 词: | 分区函数; 概率建模; 随机场 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-15：cwx |
| 阅读次数: | 52 |