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» Using Machine Learning to Focus Iterative Optimization
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ICML
2007
IEEE
16 years 4 months ago
On learning linear ranking functions for beam search
Beam search is used to maintain tractability in large search spaces at the expense of completeness and optimality. We study supervised learning of linear ranking functions for con...
Yuehua Xu, Alan Fern
ML
2007
ACM
104views Machine Learning» more  ML 2007»
15 years 2 months ago
A general criterion and an algorithmic framework for learning in multi-agent systems
We offer a new formal criterion for agent-centric learning in multi-agent systems, that is, learning that maximizes one’s rewards in the presence of other agents who might also...
Rob Powers, Yoav Shoham, Thuc Vu
GECCO
2006
Springer
177views Optimization» more  GECCO 2006»
15 years 6 months ago
Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure
The learning classifier system XCS is an iterative rulelearning system that evolves rule structures based on gradient-based prediction and rule quality estimates. Besides classifi...
Martin V. Butz, Pier Luca Lanzi, Stewart W. Wilson
COLT
2000
Springer
15 years 7 months ago
The Computational Complexity of Densest Region Detection
We investigate the computational complexity of the task of detecting dense regions of an unknown distribution from un-labeled samples of this distribution. We introduce a formal l...
Shai Ben-David, Nadav Eiron, Hans-Ulrich Simon
COLT
2004
Springer
15 years 8 months ago
Reinforcement Learning for Average Reward Zero-Sum Games
Abstract. We consider Reinforcement Learning for average reward zerosum stochastic games. We present and analyze two algorithms. The first is based on relative Q-learning and the ...
Shie Mannor