Efficient Online Multiclass Prediction On Graphs Via Surrogate Losses
ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 54(2017)
摘要
We develop computationally efficient algorithms for online multi-class prediction. Our construction is based on carefully-chosen data-dependent surrogate loss functions, and the new methods enjoy strong mistake bound guarantees.To illustrate the technique, we study the combinatorial problem of node classification and develop a prediction strategy that is linear-time per round. In contrast, the offline benchmark is NP-hard to compute in general. We demonstrate the empirical performance of the method on several datasets.
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