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139
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ICML
2009
IEEE
16 years 4 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
140
Voted
ECAI
2006
Springer
15 years 7 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
166
Voted
NN
2000
Springer
192views Neural Networks» more  NN 2000»
15 years 3 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...
153
Voted
CVPR
2007
IEEE
16 years 5 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 ...
101
Voted
ICML
2006
IEEE
16 years 4 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