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NIPS
2001
13 years 8 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
ICML
1997
IEEE
14 years 8 months ago
Predicting Multiprocessor Memory Access Patterns with Learning Models
Machine learning techniques are applicable to computer system optimization. We show that shared memory multiprocessors can successfully utilize machine learning algorithms for mem...
M. F. Sakr, Steven P. Levitan, Donald M. Chiarulli...
NN
2010
Springer
187views Neural Networks» more  NN 2010»
13 years 2 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...
ILP
2003
Springer
14 years 18 days ago
Complexity Parameters for First-Order Classes
We study several complexity parameters for first order formulas and their suitability for first order learning models. We show that the standard notion of size is not captured by...
Marta Arias, Roni Khardon
IJCNN
2007
IEEE
14 years 1 months ago
Range Data Approximation for Mobile Robot by Using CAN2
— In this article, we apply the competitive associative net called CAN2 to the processing of the range data of indoor environment acquired by a mobile robot, where the CAN2 is a ...
Takeshi Nishida, Shuichi Kurogi, Yuji Takemura, Hi...