大规模半监督学习][Large-Scale Semi-Supervised Learning]

大规模半监督学习 Large-Scale Semi-Supervised Learning


课程网址:	http://videolectures.net/mmdss07_weston_lsssl/
主讲教师:	Jason Weston
开课单位:	美国NEC实验室
开课时间:	2007-11-26
课程语种:	英语
中文简介:	标记数据是昂贵的，而未标记数据往往是丰富和便宜的收集。可以同时使用这两种数据的半监督学习算法的性能明显优于单独使用标记数据的监督算法。但是，为了观察到这些收益，培训的未标记数据量应该相对较大。因此，使半监督算法具有可扩展性是至关重要的。在这项工作中，我们讨论了一些最近的技术，以提高这些算法的缩放能力。
课程简介:	Labeling data is expensive, whilst unlabeled data is often abundant and cheap to collect. Semi-supervised learning algorithms that can use both types of data can perform significantly better than supervised algorithms that use labeled data alone. However, for such gains to be observed, the amount of unlabeled data trained on should be relatively large. Therefore, making semi-supervised algorithms scalable is paramount. In this work we discuss several recent techniques for improving the scaling ability of these algorithms.
关键词:	标签数据; 半监督学习算法; 可扩展性; 缩放能力
课程来源:	视频讲座网
最后编审:	2021-05-14：yumf
阅读次数:	43

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