图 书 馆
部分可观测的最大熵辨别马尔可夫网络Partially Observed Maximum Entropy Discrimination Markov Networks |
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| 课程网址: | http://videolectures.net/cmulls08_zhu_pome/ |
| 主讲教师: | Jun Zhu |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2009-01-15 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unattainable fully annotated training data. While likelihood-based methods have been extensively explored, to our knowledge, learning structured prediction models with latent variables based on the max-margin principle remains largely an open problem. In this paper, we present a partially observed Maximum Entropy Discrimination Markov Network (PoMEN) model that attempts to combine the advantages of Bayesian and margin based paradigms for learning Markov networks from partially labeled data. PoMEN leads to an averaging prediction rule that resembles a Bayes predictor that is more robust to overfitting, but is also built on the desirable discriminative laws resemble those of the M$^3$N. We develop an EM-style algorithm utilizing existing convex optimization algorithms for M$^3$N as a subroutine. We demonstrate competent performance of PoMEN over existing methods on a real-world web data extraction task. |
| 关 键 词: | 计算机科学; 机械学习; 马尔科夫过程 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 42 |