图 书 馆
网络数据推理与学习Inference and Learning with Networked Data |
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| 课程网址: | http://videolectures.net/mmdss07_provost_ilwn/ |
| 主讲教师: | Foster Provost |
| 开课单位: | 纽约大学 |
| 开课时间: | 2008-01-15 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In many applications we would like to draw inferences about entities that are interconnected in complex networks. For example, calls, emails, IM, and web pointers link people into huge social networks. However, traditional statistical and machine learning classification methods assume that entities are independent of each other. I start by discussing various applications of "classification" (scoring) in networked data, from fraud detection to counterterrorism to network-based marketing. I then discuss four characteristics of networked data that allow improvements-- sometimes substantial--over traditional classification: (i) models can take into account "guilt by association," (ii) inference can be performed "collectively," whereby inferences on linked entities mutually reinforce each other, (iii) characteristics of linked entities can be incorporated in models, and (iv) models can incorporate specific identifiers, such as the identities of particular individuals, to improve inference. I present results demonstrating the effectiveness of these techniques. |
| 关 键 词: | 复杂网络; 应用程序; 机器学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-24：cwx |
| 阅读次数: | 28 |