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ICML
2003
IEEE
15 years 10 months ago
Relativized Options: Choosing the Right Transformation
Relativized options combine model minimization methods and a hierarchical reinforcement learning framework to derive compact reduced representations of a related family of tasks. ...
Balaraman Ravindran, Andrew G. Barto
ICML
2008
IEEE
15 years 10 months ago
Apprenticeship learning using linear programming
In apprenticeship learning, the goal is to learn a policy in a Markov decision process that is at least as good as a policy demonstrated by an expert. The difficulty arises in tha...
Umar Syed, Michael H. Bowling, Robert E. Schapire
ICML
2009
IEEE
15 years 4 months ago
Learning linear dynamical systems without sequence information
Virtually all methods of learning dynamic systems from data start from the same basic assumption: that the learning algorithm will be provided with a sequence, or trajectory, of d...
Tzu-Kuo Huang, Jeff Schneider
COLT
1998
Springer
15 years 2 months ago
Learning One-Variable Pattern Languages in Linear Average Time
A new algorithm for learning one-variable pattern languages is proposed and analyzed with respect to its average-case behavior. We consider the total learning time that takes into...
Rüdiger Reischuk, Thomas Zeugmann
ICCV
2007
IEEE
15 years 4 months ago
Laplacian PCA and Its Applications
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
Deli Zhao, Zhouchen Lin, Xiaoou Tang