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» Learning Monotonic Linear Functions
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ICML
2007
IEEE
14 years 8 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
AAMAS
2007
Springer
13 years 7 months ago
Parallel Reinforcement Learning with Linear Function Approximation
In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal policies for single-agent RL problems more quickly by us...
Matthew Grounds, Daniel Kudenko
MM
2003
ACM
113views Multimedia» more  MM 2003»
14 years 26 days ago
The combination limit in multimedia retrieval
Combining search results from multimedia sources is crucial for dealing with heterogeneous multimedia data, particularly in multimedia retrieval where a final ranked list of item...
Rong Yan, Alexander G. Hauptmann
TIT
2002
72views more  TIT 2002»
13 years 7 months ago
Universal codes for finite sequences of integers drawn from a monotone distribution
We offer two noiseless codes for blocks of integers Xn = (X1, . . . , Xn). We provide explicit bounds on the relative redundancy that are valid for any distribution F in the class...
Dean P. Foster, Robert A. Stine, Abraham J. Wyner
AIPS
2008
13 years 10 months ago
Learning Heuristic Functions through Approximate Linear Programming
Planning problems are often formulated as heuristic search. The choice of the heuristic function plays a significant role in the performance of planning systems, but a good heuris...
Marek Petrik, Shlomo Zilberstein