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ICANN
2009
Springer
15 years 9 months ago
Constrained Learning Vector Quantization or Relaxed k-Separability
Neural networks and other sophisticated machine learning algorithms frequently miss simple solutions that can be discovered by a more constrained learning methods. Transition from ...
Marek Grochowski, Wlodzislaw Duch
IWANN
1999
Springer
15 years 9 months ago
Adaptive Resonance Theory Microchips
Recently, a real-time clustering microchip based on the ART1 algorithm has been reported. That chip was able to classify 100-bit input patterns into up to 18 categories. However, i...
Teresa Serrano-Gotarredona, Bernabé Linares...
NC
1998
102views Neural Networks» more  NC 1998»
15 years 6 months ago
Outliers and Bayesian Inference
In this paper we report about an investigation in which we studied the properties of Bayes' inferred neural network classifiers in the context of outlier detection. The proble...
Peter Sykacek
TSMC
2008
140views more  TSMC 2008»
15 years 5 months ago
Adaptive Feedback Control by Constrained Approximate Dynamic Programming
A constrained approximate dynamic programming (ADP) approach is presented for designing adaptive neural network (NN) controllers with closed-loop stability and performance guarante...
S. Ferrari, J. E. Steck, R. Chandramohan
185
Voted
NN
2007
Springer
105views Neural Networks» more  NN 2007»
15 years 4 months ago
Guiding exploration by pre-existing knowledge without modifying reward
Reinforcement learning is based on exploration of the environment and receiving reward that indicates which actions taken by the agent are good and which ones are bad. In many app...
Kary Främling