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PKDD
2009
Springer
152views Data Mining» more  PKDD 2009»
15 years 4 months ago
Feature Selection for Value Function Approximation Using Bayesian Model Selection
Abstract. Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of th...
Tobias Jung, Peter Stone
SIGIR
2012
ACM
12 years 12 months ago
Active query selection for learning rankers
Methods that reduce the amount of labeled data needed for training have focused more on selecting which documents to label than on which queries should be labeled. One exception t...
Mustafa Bilgic, Paul N. Bennett
76
Voted
IJON
2007
104views more  IJON 2007»
14 years 9 months ago
A probabilistic model of eye movements in concept formation
It has been unclear whether optimal experimental design accounts of data selection may offer insight into evidence acquisition tasks in which the learner’s beliefs change greatl...
Jonathan D. Nelson, Garrison W. Cottrell
67
Voted
CGO
2007
IEEE
15 years 3 months ago
Rapidly Selecting Good Compiler Optimizations using Performance Counters
Applying the right compiler optimizations to a particular program can have a significant impact on program performance. Due to the non-linear interaction of compiler optimization...
John Cavazos, Grigori Fursin, Felix V. Agakov, Edw...
DAGM
2006
Springer
15 years 1 months ago
Parameterless Isomap with Adaptive Neighborhood Selection
Abstract. Isomap is a highly popular manifold learning and dimensionality reduction technique that effectively performs multidimensional scaling on estimates of geodesic distances....
Nathan Mekuz, John K. Tsotsos