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» Feature selection with neural networks
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IWANN
1999
Springer
14 years 1 months ago
Using Temporal Neighborhoods to Adapt Function Approximators in Reinforcement Learning
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
R. Matthew Kretchmar, Charles W. Anderson
NN
2006
Springer
122views Neural Networks» more  NN 2006»
13 years 8 months ago
Goals and means in action observation: A computational approach
Many of our daily activities are supported by behavioural goals that guide the selection of actions, which allow us to reach these goals effectively. Goals are considered to be im...
Raymond H. Cuijpers, Hein T. van Schie, Mathieu Ko...
NECO
2007
258views more  NECO 2007»
13 years 8 months ago
Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity
The persistent modification of synaptic efficacy as a function of the relative timing of pre- and postsynaptic spikes is a phenomenon known as spiketiming-dependent plasticity (...
Razvan V. Florian
CIKM
2007
Springer
14 years 2 months ago
Improving the classification of newsgroup messages through social network analysis
Newsgroup participants interact with their communities through conversation threads. They may respond to a message to answer a question, debate a topic, support or disagree with a...
Blaz Fortuna, Eduarda Mendes Rodrigues, Natasa Mil...
ISNN
2007
Springer
14 years 2 months ago
Hybrid Intelligent Modeling Approach for the Ball Mill Grinding Process
Modeling for the ball mill grinding process is still an imperative but difficult problem for the optimal control of mineral processing industry. Due to the integrated complexities ...
Ming Tie, Jing Bi, Yushun Fan