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ICML
2009
IEEE
14 years 8 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
ECAI
2006
Springer
13 years 11 months ago
Least Squares SVM for Least Squares TD Learning
Abstract. We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible ...
Tobias Jung, Daniel Polani
NN
2000
Springer
192views Neural Networks» more  NN 2000»
13 years 7 months ago
A new algorithm for learning in piecewise-linear neural networks
Piecewise-linear (PWL) neural networks are widely known for their amenability to digital implementation. This paper presents a new algorithm for learning in PWL networks consistin...
Emad Gad, Amir F. Atiya, Samir I. Shaheen, Ayman E...
CVPR
2007
IEEE
14 years 9 months ago
Differential Camera Tracking through Linearizing the Local Appearance Manifold
The appearance of a scene is a function of the scene contents, the lighting, and the camera pose. A set of n-pixel images of a non-degenerate scene captured from different perspec...
Hua Yang, Marc Pollefeys, Greg Welch, Jan-Michael ...
ICML
2006
IEEE
14 years 8 months ago
Regression with the optimised combination technique
We consider the sparse grid combination technique for regression, which we regard as a problem of function reconstruction in some given function space. We use a regularised least ...
Jochen Garcke