Graph-based semi-supervised and active learning for edge flows
We present a graph-based semi-supervised learning (SSL) method for learning edge flows defined on a graph. Specifically, given flow measurements on a subset of edges, we want to predict the flows on the remaining edges. To this end, we develop a computational framework that imposes certain constrain...
| Main Authors: | , , , |
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| Format: | Conference item |
| Published: |
Association for Computing Machinery
2019
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