图 书 馆
复杂性推理学习偏差Inference Complexity as Learning Bias |
|
| 课程网址: | http://videolectures.net/cmulls08_domingos_icl/ |
| 主讲教师: | Pedro Domingos |
| 开课单位: | 华盛顿大学 |
| 开课时间: | 2009-01-15 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, because it requires approximate inference. We propose an alternative: learning models with a score function that directly penalizes the cost of inference. Specifically, we learn arithmetic circuits with a penalty on the number of edges in the circuit (in which the cost of inference is linear). Our algorithm is equivalent to learning a Bayesian network with context-specific independence by greedily splitting conditional distributions, at each step scoring the candidates by compiling the resulting network into an arithmetic circuit, and using its size as the penalty. We show how this can be done efficiently, without compiling a circuit from scratch for each candidate. Experiments on several real-world domains show that our algorithm is able to learn tractable models with very large treewidth, and yields more accurate predictions than a standard context-specific Bayesian network learner, in far less time. (Joint work with Daniel Lowd.) |
| 关 键 词: | 贝叶斯网络; 图形模型; 电路 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-02-10：nkq |
| 阅读次数: | 36 |