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Beyond Banditron: A Conservative and Efficient Reduction for Online Multiclass Prediction with Bandit Setting Model
2009
2009 Ninth IEEE International Conference on Data Mining
In this paper, we consider a recently proposed supervised learning problem, called online multiclass prediction with bandit setting model. Aiming at learning from partial feedback of online classification results, i.e. "true" when the predicting label is right or "false" when the predicting label is wrong, this new kind of problems arouses much of researchers' interest due to its close relations to real world internet applications and human cognitive procedure. While some algorithms have been
doi:10.1109/icdm.2009.36
dblp:conf/icdm/ChenCZCZ09
fatcat:duiwvhsjxzanrppwbd63yfo3f4