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CONTEXT
2001
Springer
15 years 7 months ago
A Connectionist-Symbolic Approach to Modeling Agent Behavior: Neural Networks Grouped by Contexts
A recent report by the National Research Council (NRC) declares neural networks “hold the most promise for providing powerful learning models”. While some researchers have expe...
Amy E. Henninger, Avelino J. Gonzalez, Michael Geo...
148
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APIN
1998
139views more  APIN 1998»
15 years 3 months ago
Evolutionary Learning of Modular Neural Networks with Genetic Programming
Evolutionary design of neural networks has shown a great potential as a powerful optimization tool. However, most evolutionary neural networks have not taken advantage of the fact ...
Sung-Bae Cho, Katsunori Shimohara
TNN
2008
177views more  TNN 2008»
15 years 3 months ago
Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model
Previous work on statistical language modeling has shown that it is possible to train a feed-forward neural network to approximate probabilities over sequences of words, resulting...
Yoshua Bengio, Jean-Sébastien Senecal
ICANN
2010
Springer
15 years 4 months ago
Cell Microscopic Segmentation with Spiking Neuron Networks
Abstract. Spiking Neuron Networks (SNNs) overcome the computational power of neural networks made of thresholds or sigmoidal units. Indeed, SNNs add a new dimension, the temporal a...
Boudjelal Meftah, Olivier Lezoray, Michel Lecluse,...
PPSN
1992
Springer
15 years 7 months ago
Structure Evolution and Incomplete Induction
We present an application of arti cial neural networks to machine condition monitoring. Since several signal preprocessing methods produce high dimensional feature vectors there i...
Reinhard Lohmann