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结构化网络预测的距离度量学习Learning a Distance Metric for Structured Network Prediction |
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| 课程网址: | http://videolectures.net/lce06_andrews_ladm/ |
| 主讲教师: | Stuart Andrews |
| 开课单位: | 哥伦比亚大学 |
| 开课时间: | 2007-04-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Man-made or naturally-formed networks typically exhibit a high degree of structural regularity. In this paper, we introduce the problem of structured network prediction: given a set of n entities and a desired distribution for connectivity, return a likely set of edges connecting the entities together in a network having the specified degree distribution. Prediction is useful for initializing a network, augmenting an existing network, and for filtering existing networks, when the structure of the network is known. In order to capture the inter-dependencies amongst pairwise predictions to learn parameters of our model, we build upon recent structured output models. Novel in our approach is the use of partially labeled training examples, and a network structure sensitive loss function. We present encouraging results of the model predicting equivalence graphs and links in a social network. |
| 关 键 词: | 结构化网络; 预测社交网络; 等价图 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-12：lxf |
| 阅读次数: | 125 |