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» State-Space Inference and Learning with Gaussian Processes
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PKDD
2009
Springer
152views Data Mining» more  PKDD 2009»
15 years 6 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
ICIP
2008
IEEE
16 years 1 months ago
Implicit spatial inference with sparse local features
This paper introduces a novel way to leverage the implicit geometry of sparse local features (e.g. SIFT operator) for the purposes of object detection and segmentation. A two-clas...
Deirdre O'Regan, Anil C. Kokaram
IROS
2008
IEEE
144views Robotics» more  IROS 2008»
15 years 5 months ago
Learning nonparametric policies by imitation
— A long cherished goal in artificial intelligence has been the ability to endow a robot with the capacity to learn and generalize skills from watching a human teacher. Such an ...
David B. Grimes, Rajesh P. N. Rao
ICRA
2010
IEEE
104views Robotics» more  ICRA 2010»
14 years 10 months ago
Using model knowledge for learning inverse dynamics
— In recent years, learning models from data has become an increasingly interesting tool for robotics, as it allows straightforward and accurate model approximation. However, in ...
Duy Nguyen-Tuong, Jan Peters
ICML
2009
IEEE
16 years 6 days ago
Archipelago: nonparametric Bayesian semi-supervised learning
Semi-supervised learning (SSL), is classification where additional unlabeled data can be used to improve accuracy. Generative approaches are appealing in this situation, as a mode...
Ryan Prescott Adams, Zoubin Ghahramani