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AUSAI
2004
Springer
14 years 1 months ago
A Dynamic Allocation Method of Basis Functions in Reinforcement Learning
In this paper, we propose a dynamic allocation method of basis functions, an Allocation/Elimination Gaussian Softmax Basis Function Network (AE-GSBFN), that is used in reinforcemen...
Shingo Iida, Kiyotake Kuwayama, Masayoshi Kanoh, S...
AAAI
2011
12 years 7 months ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
GECCO
2008
Springer
159views Optimization» more  GECCO 2008»
13 years 8 months ago
IGAP: interactive genetic algorithm peer to peer
We present IGAP, a peer to peer interactive genetic algorithm which reflects the real world methodology followed in team design. We apply our methodology to floorplanning. Throu...
Juan C. Quiroz, Amit Banerjee, Sushil J. Louis
COMCOM
2006
118views more  COMCOM 2006»
13 years 7 months ago
A novel content-aware interleaving for wireless video transmission
In wireless network environments, the effect of transmission errors and losses on the video quality vary, depending on the intensity of the burst, and which parts of the video str...
Jeong-Yong Choi, Jitae Shin
IJCNN
2008
IEEE
14 years 2 months ago
Uncertainty propagation for quality assurance in Reinforcement Learning
— In this paper we address the reliability of policies derived by Reinforcement Learning on a limited amount of observations. This can be done in a principled manner by taking in...
Daniel Schneegaß, Steffen Udluft, Thomas Mar...