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» On learning with dissimilarity functions
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NIPS
2001
13 years 10 months ago
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
O. Donchin, Reza Shadmehr
NN
2010
Springer
187views Neural Networks» more  NN 2010»
13 years 3 months ago
Efficient exploration through active learning for value function approximation in reinforcement learning
Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares ...
Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiya...
ICML
2007
IEEE
14 years 10 months ago
Automatic shaping and decomposition of reward functions
This paper investigates the problem of automatically learning how to restructure the reward function of a Markov decision process so as to speed up reinforcement learning. We begi...
Bhaskara Marthi
COLT
2003
Springer
14 years 2 months ago
Learning All Subfunctions of a Function
Sublearning, a model for learning of subconcepts of a concept, is presented. Sublearning a class of total recursive functions informally means to learn all functions from that cla...
Sanjay Jain, Efim B. Kinber, Rolf Wiehagen
TIP
2002
81views more  TIP 2002»
13 years 8 months ago
Fractal image compression with region-based functionality
Region-based functionality offered by the MPEG-4 video compression standard is also appealing for still images, for example to permit object-based queries of a still-image database...
Kamel Belloulata, Janusz Konrad