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VideoLectures.net案例研究

VideoLectures.net case study
课程网址: http://videolectures.net/taoiw09_grcar_videolectures/  
主讲教师: Miha Grčar, Peter Keše
开课单位: 约瑟夫·斯特凡学院
开课时间: 2009-02-09
课程语种: 英语
中文简介:
视频讲座案例研究的任务是开发一个软件组件, 帮助视频讲座编辑对录制的讲座 (即本体群体) 进行分类。由于主办讲座数量的迅速增长, 以及分类分类相当细粒度 (200个类别和不断增长), 因此需要这一功能。除了协助对新讲座进行分类外, 该软件还将用于对已经分类的讲座进行重新分类和额外分类。我们将展示我们在任务中的成功, 因为分类程序非常精确 – 它实现了比基线 – 12–20 的精度, 并且在丢失数据方面具有高度的鲁棒性。后一种意味着讲座可能缺少文本注释 (如说明和幻灯片标题), 但仍对其进行了正确分类。此外, 分类程序已成功地纳入视频讲座网站。分类面板中的分类建议 (称为 "快速链接") 提供给作者。
课程简介: The task in the VideoLectures case study is to develop a software component that will aid the VideoLectures editors in categorizing recorded lectures (i.e. ontology population). This functionality is required due to the rapid growth of the number of hosted lectures as well as due to the fact that the categorization taxonomy is rather fine-grained (200 categories and growing). In addition to aiding the categorization of new lectures, the software will also be used for re-categorization and additional categorization of lectures already categorized. We will show that we were successful in our task as the categorizer is highly accurate – it achieves accuracies that stretch 12–20% above the baseline – and highly robust in terms of missing data. The latter means that a lecture might be missing textual annotations (such as the description and slide titles) but is still categorized correctly. Furthermore, the categorizer has been successfully integrated into the VideoLectures Web site. Categorization suggestions (termed "quick links") are provided to the author in the categorization panel.
关 键 词: 机器学习; 网络分析; 数据挖掘
课程来源: 视频讲座网
最后编审: 2020-07-28:yumf
阅读次数: 37