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ECML
2007
Springer
13 years 11 months ago
Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass
NIPS
1994
13 years 8 months ago
Generalization in Reinforcement Learning: Safely Approximating the Value Function
To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to t...
Justin A. Boyan, Andrew W. Moore
ECML
2007
Springer
14 years 1 months ago
On Phase Transitions in Learning Sparse Networks
In this paper we study the identification of sparse interaction networks as a machine learning problem. Sparsity means that we are provided with a small data set and a high number...
Goele Hollanders, Geert Jan Bex, Marc Gyssens, Ron...
ICALT
2006
IEEE
14 years 1 months ago
Mobile Learning: Is Anytime + Anywhere = Always Online?
The new e-learning trend, called mobile learning opens variety of questions to solve. To allow access to learning content anytime and anyplace a technique called hoarding is somet...
Anna Trifonova, Marco Ronchetti
AINA
2008
IEEE
14 years 1 months ago
Missing Value Estimation for Time Series Microarray Data Using Linear Dynamical Systems Modeling
The analysis of gene expression time series obtained from microarray experiments can be effectively exploited to understand a wide range of biological phenomena from the homeostat...
Connie Phong, Raul Singh