图 书 馆
关系结构的生成模型Generative Models for Relational Structures |
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| 课程网址: | http://videolectures.net/ssspr2010_hancock_gmr/ |
| 主讲教师: | Edwin Hancock |
| 开课单位: | 纽约大学 |
| 开课时间: | 2010-09-13 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present a method for constructing a generative model for sets of graphs by adopting a minimum description length approach. The method is posed in terms of learning a generative supergraph model from which the new samples can be obtained by Gibbs sampling. We commence by constructing a probability distribution for the occurrence of nodes and edges over the supergraph. We encode the complexity of the supergraph using the von-Neumann entropy. A variant of EM algorithm is developed to minimize the description length criterion in which the node correspondences between the sample graphs and the supergraph are treated as missing data.The maximization step involves updating both the node correspondence information and the structure of supergraph using graduated assignment. Empirical evaluations on real data reveal the practical utility of our proposed algorithm and show that our generative model gives good graph classification results. |
| 关 键 词: | 关系; 结构; 生成模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-27：zyk |
| 阅读次数: | 35 |