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» Tracking the Best of Many Experts
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COLT
2005
Springer
13 years 10 months ago
Tracking the Best of Many Experts
András György, Tamás Linder, G&...
ICML
1995
IEEE
14 years 5 months ago
Tracking the Best Expert
Mark Herbster, Manfred K. Warmuth
COLT
2007
Springer
13 years 10 months ago
Regret to the Best vs. Regret to the Average
Abstract. We study online regret minimization algorithms in a bicriteria setting, examining not only the standard notion of regret to the best expert, but also the regret to the av...
Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, ...
COLT
2001
Springer
13 years 9 months ago
Tracking a Small Set of Experts by Mixing Past Posteriors
In this paper, we examine on-line learning problems in which the target concept is allowed to change over time. In each trial a master algorithm receives predictions from a large ...
Olivier Bousquet, Manfred K. Warmuth
ICDM
2003
IEEE
181views Data Mining» more  ICDM 2003»
13 years 10 months ago
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
Algorithms for tracking concept drift are important for many applications. We present a general method based on the Weighted Majority algorithm for using any online learner for co...
Jeremy Z. Kolter, Marcus A. Maloof