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大规模学习和推理：我们用马尔可夫逻辑网络学到的东西Large-Scale Learning and Inference: What We Have Learned with Markov Logic Networks |
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| 课程网址: | http://videolectures.net/nipsworkshops09_domingos_lsliwwlmln/ |
| 主讲教师: | Pedro Domingos |
| 开课单位: | 华盛顿大学 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Markov logic allows very large and rich graphical models to be compactly specified. Current learning and inference algorithms for Markov logic can routinely handle models with millions of variables, billions of features, thousands of latent variables, and strong dependencies. In this talk I will give an overview of the main ideas in these algorithms, including weighted satisfiability, MCMC with deterministic dependencies, lazy inference, lifted inference, relational cutting planes, scaled conjugate gradient, relational clustering and relational pathfinding. I will also discuss the lessons learned in developing successive generations of these algorithms and promising ideas for the next round of scaling up. |
| 关 键 词: | 马尔可夫; 图形模型; 推理算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-07：lxf |
| 阅读次数: | 58 |