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» Bounding the cost of learned rules
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AI
2000
Springer
13 years 7 months ago
Bounding the cost of learned rules
Jihie Kim, Paul S. Rosenbloom
ALT
2002
Springer
14 years 4 months ago
How to Achieve Minimax Expected Kullback-Leibler Distance from an Unknown Finite Distribution
Abstract. We consider a problem that is related to the “Universal Encoding Problem” from information theory. The basic goal is to find rules that map “partial information”...
Dietrich Braess, Jürgen Forster, Tomas Sauer,...
SIGECOM
2010
ACM
149views ECommerce» more  SIGECOM 2010»
14 years 4 days ago
A new understanding of prediction markets via no-regret learning
We explore the striking mathematical connections that exist between market scoring rules, cost function based prediction markets, and no-regret learning. We first show that any c...
Yiling Chen, Jennifer Wortman Vaughan
COLT
2008
Springer
13 years 9 months ago
Extracting Certainty from Uncertainty: Regret Bounded by Variation in Costs
Prediction from expert advice is a fundamental problem in machine learning. A major pillar of the field is the existence of learning algorithms whose average loss approaches that ...
Elad Hazan, Satyen Kale
ICML
2004
IEEE
14 years 23 days ago
Towards tight bounds for rule learning
While there is a lot of empirical evidence showing that traditional rule learning approaches work well in practice, it is nearly impossible to derive analytical results about thei...
Ulrich Rückert, Stefan Kramer