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SAGT
2009
Springer
192views Game Theory» more  SAGT 2009»
14 years 2 months ago
Learning and Approximating the Optimal Strategy to Commit To
Computing optimal Stackelberg strategies in general two-player Bayesian games (not to be confused with Stackelberg strategies in routing games) is a topic that has recently been ga...
Joshua Letchford, Vincent Conitzer, Kamesh Munagal...
CVPR
2007
IEEE
14 years 9 months ago
Utilizing Variational Optimization to Learn Markov Random Fields
Markov Random Field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an M...
Marshall F. Tappen
IJCNN
2008
IEEE
14 years 2 months ago
On the learning of nonlinear visual features from natural images by optimizing response energies
— The operation of V1 simple cells in primates has been traditionally modelled with linear models resembling Gabor filters, whereas the functionality of subsequent visual cortic...
Jussi T. Lindgren, Aapo Hyvärinen
IJCAI
2001
13 years 9 months ago
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
Ronen I. Brafman, Moshe Tennenholtz
ECOOP
1991
Springer
13 years 11 months ago
Incremental Class Dictionary Learning and Optimization
We have previously shown how the discovery of classes from objects can be automated, and how the resulting class organization can be e ciently optimized in the case where the opti...
Paul L. Bergstein, Karl J. Lieberherr