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Efficient Algorithms for Combinatorial Online Prediction
Bibliographic Information
- Published
- 2013
- Resource Type
- journal article
- DOI
-
- 10.1007/978-3-642-40935-6_3
- Publisher
- Springer Berlin Heidelberg
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Description
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.
Journal
-
- Lecture Notes in Computer Science
-
Lecture Notes in Computer Science 22-32, 2013
Springer Berlin Heidelberg
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Details 詳細情報について
-
- CRID
- 1360848655628362240
-
- ISSN
- 16113349
- 03029743
-
- Article Type
- journal article
-
- Data Source
-
- Crossref
- KAKEN
- OpenAIRE
