图 书 馆
通过对称分区函数进行高效的高维最大熵建模Efficient high dimensional maximum entropy modeling via symmetric partition functions |
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| 课程网址: | http://videolectures.net/machine_vernaza_entropy_modeling/ |
| 主讲教师: | Paul Vernaza |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2013-01-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The application of the maximum entropy principle to sequence modeling has been popularized by methods such as Conditional Random Fields (CRFs). However, these approaches are generally limited to modeling paths in discrete spaces of low dimensionality. We consider the problem of modeling distributions over paths in continuous spaces of high dimensionality - a problem for which inference is generally intractable. Our main contribution is to show that maximum entropy modeling of high-dimensional, continuous paths is tractable as long as the constrained features possess a certain kind of low dimensional structure. In this case, we show that the associated {\em partition function} is symmetric and that this symmetry can be exploited to compute the partition function efficiently in a compressed form. Empirical results are given showing an application of our method to maximum entropy modeling of high dimensional human motion capture data. |
| 关 键 词: | 最大熵原理; 条件随机场; 路径建模分布 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 55 |