图 书 馆
使用概率模型分析文本和社交网络数据Analyzing Text and Social Network Data with Probabilistic Models |
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| 课程网址: | http://videolectures.net/ecmlpkdd2012_smyth_probabilistic_models/ |
| 主讲教师: | Padhraic Smyth |
| 开课单位: | 加州大学欧文分校 |
| 开课时间: | 2012-10-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Exploring and understanding large text and social network data sets is of increasing interest across multiple fields, in computer science, social science, history, medicine, and more. This talk will present an overview of recent work using probabilistic latent variable models to analyze such data. Latent variable models have a long tradition in data analysis and typically hypothesize the existence of simple unobserved phenomena to explain relatively complex observed data. In the past decade there has been substantial work on extending the scope of these approaches from relatively small simple data sets to much more complex text and network data. We will discuss the basic concepts behind these developments, reviewing key ideas, recent advances, and open issues. In addition we will highlight common ideas that lie beneath the surface of different approaches including links (for example) to work in matrix factorization. The concluding part of the talk will focus more specifically on recent work with temporal social networks, specifically data in the form of time-stamped events between nodes (such as emails exchanged among individuals over time). |
| 关 键 词: | 社交网络数据集; 计算机科学; 概率潜在变量; 模型分析; 观测数据; 矩阵分解 |
| 课程来源: | 视频讲座网公开课 |
| 最后编审: | 2019-05-26：cwx |
| 阅读次数: | 51 |