图 书 馆
主题模型中的内存有界推断Memory Bounded Inference in Topic Models |
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| 课程网址: | http://videolectures.net/icml08_gomes_mbi/ |
| 主讲教师: | Ryan Gomes |
| 开课单位: | 加利福尼亚理工学院 |
| 开课时间: | 2008-07-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | What type of algorithms and statistical techniques support learning from very large datasets over long stretches of time? We address this question through a memory bounded version of a variational EM algorithm that approximates inference of a topic model. The algorithm alternates two phases: "model building" and "model compression" in order to always satisfy a given memory constraint. The model building phase grows its internal representation (the number of topics) as more data arrives through Bayesian model selection. Compression is achieved by merging data-items in clumps and only caching their sufficient statistics. Empirically, the resulting algorithm is able to handle datasets that are orders of magnitude larger than the standard batch version. |
| 关 键 词: | 统计技术支持学习; 贝叶斯模型选择; 变电磁算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-03：张荧(课程编辑志愿者) |
| 阅读次数: | 51 |