图 书 馆
纵向网络的连续时间回归模型Continuous-Time Regression Models for Longitudinal Networks |
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| 课程网址: | http://videolectures.net/nips2011_asuncion_networks/ |
| 主讲教师: | Arthur Asuncion |
| 开课单位: | 加利福尼亚大学 |
| 开课时间: | 2012-01-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The development of statistical models for continuous-time longitudinal network data is of increasing interest in machine learning and social science. Leveraging ideas from survival and event history analysis, we introduce a continuous-time regression modeling framework for network event data that can incorporate both time-dependent network statistics and time-varying regression coefficients. We also develop an efficient inference scheme that allows our approach to scale to large networks. On synthetic and real-world data, empirical results demonstrate that the proposed inference approach can accurately estimate the coefficients of the regression model, which is useful for interpreting the evolution of the network; furthermore, the learned model has systematically better predictive performance compared to standard baseline methods. |
| 关 键 词: | 纵向网络数据; 机器学习; 连续时间回归建模 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-26：cwx |
| 阅读次数: | 53 |