Semi-automatic Categorization of Videos on VideoLectures.net
Semi-automatic Categorization of Videos on VideoLectures.net
复制标题
VideoLectures.net 上视频的半自动分类
DOI：
10.1007/978-3-642-04174-7_51
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Peter Kese
中科院分区：
文献类型：
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作者：
Miha Grcar;D. Mladenić;Peter Kese
自动或半自动地将项目（例如文档）分类到分类法中是一项重要且具有挑战性的机器学习任务。在本文中，我们提出了一个半自动化的课堂视频分类模块。适当分类的讲座为用户提供了更好的浏览体验，使她更有效地访问所需的内容。我们的分类器将与讲座相关的文本中的信息和从讲座之间的各种链接中提取的信息结合在一个统一的机器学习框架中。通过考虑文本和链接，分类准确率提高了12- 20%。
Automatic or semi-automatic categorization of items (e.g. documents) into a taxonomy is an important and challenging machine-learning task. In this paper, we present a module for semi-automatic categorization of video-recorded lectures. Properly categorized lectures provide the user with a better browsing experience which makes her more efficient in accessing the desired content. Our categorizer combines information found in texts associated with lectures and information extracted from various links between lectures in a unified machine-learning framework. By taking not only texts but also the links into account, the classification accuracy is increased by 12---20%.