Semi-supervised Drifted Stream Learning with Short Lookback

Semi-supervised Drifted Stream Learning with Short Lookback
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DOI：
10.1145/3534678.3539297
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发表时间：
2022-05
期刊：
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
影响因子：
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通讯作者：
Wei-Ya Ren;Pengyang Wang;Xiaolin Li;C. Hughes;Yanjie Fu
Wei-Ya Ren;Pengyang Wang;Xiaolin Li;C. Hughes;Yanjie Fu
中科院分区：
其他
文献类型：
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作者：
Wei-Ya Ren;Pengyang Wang;Xiaolin Li;C. Hughes;Yanjie Fu

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在许多情况下，1）数据流是在真实的时间中生成的; 2）标记数据是昂贵的，并且在开始时只有有限的标记可用; 3）真实世界的数据并不总是i.i.d.数据随时间逐渐漂移; 4）历史数据的存储
In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not always i.i.d. and data drift over time gradually; 4) the storage of historical s