Abstract
We describe an algorithmic framework for an abstract game which we term a convex repeated game. We show that various online learning and boosting algorithms can be all derived as special cases of our algorithmic framework. This unified view explains the properties of existing algorithms and also enables us to derive several new interesting algorithms. Our algorithmic framework stems from a connection that we build between the notions of regret in game theory and weak duality in convex optimization.
Original language | English |
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Title of host publication | NIPS 2006 |
Subtitle of host publication | Proceedings of the 19th International Conference on Neural Information Processing Systems |
Editors | Bernhard Scholkopf, John C. Platt, Thomas Hofmann |
Publisher | MIT Press Journals |
Pages | 1265-1272 |
Number of pages | 8 |
ISBN (Electronic) | 0262195682, 9780262195683 |
State | Published - 2006 |
Event | 19th International Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, Canada Duration: 4 Dec 2006 → 7 Dec 2006 |
Publication series
Name | NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems |
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Conference
Conference | 19th International Conference on Neural Information Processing Systems, NIPS 2006 |
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Country/Territory | Canada |
City | Vancouver |
Period | 4/12/06 → 7/12/06 |
Bibliographical note
Publisher Copyright:© NIPS 2006.All rights reserved