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Regularization and feature selection in least-squares temporal difference learning
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Authors:
J. Zico Kolter
Stanford University, CA
Andrew Y. Ng
Stanford University, CA
Published in:
· Proceeding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
ACM
New York, NY
, USA
©2009
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ISBN: 978-1-60558-516-1
doi>
10.1145/1553374.1553442
2009 Article
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· Downloads (12 Months): 63
· Citation Count: 4
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Tags:
algorithms
design
feature evaluation and selection
least squares methods
linear approximation
modeling methodologies
parameter learning
performance
theory
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