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马尔可夫随机场的约束近似最大熵学习Constrained Approximate Maximum Entropy Learning of Markov Random Fields |
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| 课程网址: | http://videolectures.net/uai08_ganapathi_camel/ |
| 主讲教师: | Varun Ganapathi |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 2008-07-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Parameter estimation in Markov random fields (MRFs) is a difficult task, in which inference over the network is run in the inner loop of a gradient descent procedure. Replacing exact inference with approximate methods such as loopy belief propagation (LBP) can suffer from poor convergence. In this paper, we provide a different approach for combining MRF learning and Bethe approximation. We consider the dual of maximum likelihood Markov network learning - maximizing entropy with moment matching constraints - and then approximate both the objective and the constraints in the resulting optimization problem. Unlike previous work along these lines (Teh & Welling, 2003), our formulation allows parameter sharing between features in a general log-linear model, parameter regularization and conditional training. We show that piecewise training (Sutton & McCallum, 2005) is a very restricted special case of this formulation. We study two optimization strategies: one based on a single convex approximation and one that uses repeated convex approximations. We show results on several real-world networks that demonstrate that these algorithms can significantly outperform learning with loopy and piecewise. Our results also provide a framework for analyzing the trade-offs of different relaxations of the entropy objective and of the constraints. |
| 关 键 词: | 网络学习; 优化问题; 分段训练 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-10-11：cwx |
| 阅读次数: | 59 |