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ICML
2008
IEEE
16 years 5 months ago
An empirical evaluation of supervised learning in high dimensions
In this paper we perform an empirical evaluation of supervised learning on highdimensional data. We evaluate performance on three metrics: accuracy, AUC, and squared loss and stud...
Rich Caruana, Nikolaos Karampatziakis, Ainur Yesse...
ICML
2009
IEEE
16 years 5 months ago
Regularization and feature selection in least-squares temporal difference learning
We consider the task of reinforcement learning with linear value function approximation. Temporal difference algorithms, and in particular the Least-Squares Temporal Difference (L...
J. Zico Kolter, Andrew Y. Ng
ICRA
2009
IEEE
138views Robotics» more  ICRA 2009»
15 years 11 months ago
Which landmark is useful? Learning selection policies for navigation in unknown environments
Abstract— In general, a mobile robot that operates in unknown environments has to maintain a map and has to determine its own location given the map. This introduces significant...
Hauke Strasdat, Cyrill Stachniss, Wolfram Burgard
IROS
2008
IEEE
111views Robotics» more  IROS 2008»
15 years 11 months ago
Learning perceptual coupling for motor primitives
—Dynamic system-based motor primitives [1] have enabled robots to learn complex tasks ranging from Tennisswings to locomotion. However, to date there have been only few extension...
Jens Kober, Betty J. Mohler, Jan Peters
AAMAS
2007
Springer
15 years 10 months ago
Networks of Learning Automata and Limiting Games
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is that...
Peter Vrancx, Katja Verbeeck, Ann Nowé