Efficient Algorithms for Combinatorial Online Prediction

DOI Web Site 参考文献16件

書誌事項

公開日
2013
資源種別
journal article
DOI
  • 10.1007/978-3-642-40935-6_3
公開者
Springer Berlin Heidelberg

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説明

We study online linear optimization problems over concept classes which are defined in some combinatorial ways. Typically, those concept classes contain finite but exponentially many concepts and hence the complexity issue arises. In this paper, we survey some recent results on universal and efficient implementations of low-regret algorithmic frameworks such as Follow the Regularized Leader FTRL and Follow the Perturbed Leader FPL.

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