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Poster
Semi-Supervised Learning on Data Streams via Temporal Label Propagation
Tal Wagner · Sudipto Guha · Shiva Kasiviswanathan · Nina Mishra
We consider the problem of labeling points on a fast-moving data stream when only a small number of labeled examples are available. In our setting, incoming points must be processed efficiently and the stream is too large to store in its entirety. We present a semi-supervised learning algorithm for this task. The algorithm maintains a small synopsis of the stream which can be quickly updated as new points arrive, and labels every incoming point by provably learning from the full history of the stream. Experiments on real datasets validate that the algorithm can quickly and accurately classify points on a stream with a small quantity of labeled examples.
Author Information
Tal Wagner (MIT)
Sudipto Guha (Amazon)
Shiva Kasiviswanathan (Amazon)
Nina Mishra (Amazon)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: Semi-Supervised Learning on Data Streams via Temporal Label Propagation »
Thu. Jul 12th 01:20 -- 01:30 PM Room K11
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