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» Learning Evaluation Functions for Large Acyclic Domains
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ICCBR
2005
Springer
14 years 2 months ago
CBR for State Value Function Approximation in Reinforcement Learning
CBR is one of the techniques that can be applied to the task of approximating a function over high-dimensional, continuous spaces. In Reinforcement Learning systems a learning agen...
Thomas Gabel, Martin A. Riedmiller
ICIP
2009
IEEE
14 years 9 months ago
Learning Large Margin Likelihoods For Realtime Head Pose Tracking
We consider the problem of head tracking and pose estimation in realtime from low resolution images. Tracking and pose recognition are treated as two coupled problems in a probabi...
PKDD
2009
Springer
103views Data Mining» more  PKDD 2009»
14 years 3 months ago
Kernels for Periodic Time Series Arising in Astronomy
Abstract. We present a method for applying machine learning algorithms to the automatic classification of astronomy star surveys using time series of star brightness. Currently su...
Gabriel Wachman, Roni Khardon, Pavlos Protopapas, ...
AINA
2010
IEEE
14 years 1 months ago
Routing Loops in DAG-Based Low Power and Lossy Networks
Abstract—Directed Acyclic Graphs (DAGs), rooted at popular/default destinations, have emerged as a preferred mechanism to provide IPv6 routing functionality in large scale low po...
Weigao Xie, Mukul Goyal, Hossein Hosseini, Jerald ...
ICMLA
2010
13 years 6 months ago
Multi-Agent Inverse Reinforcement Learning
Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship l...
Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah,...