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» Neural Networks and Complexity Theory
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IJCAI
2007
14 years 11 months ago
Direct Code Access in Self-Organizing Neural Networks for Reinforcement Learning
TD-FALCON is a self-organizing neural network that incorporates Temporal Difference (TD) methods for reinforcement learning. Despite the advantages of fast and stable learning, TD...
Ah-Hwee Tan
ETFA
2008
IEEE
15 years 4 months ago
Gesture recognition using evolution strategy neural network
A new approach to interact with an industrial robot using hand gestures is presented. System proposed here can learn a first time user’s hand gestures rapidly. This improves pro...
Johan Hägg, Baran Çürükl&uum...
IJCNN
2006
IEEE
15 years 3 months ago
Ensemble of Neural Network Emulations for Climate Model Physics: The Impact on Climate Simulations
—A new application of the NN ensemble approach is presented. It is applied to NN emulations of model physics in complex numerical climate models, and aimed at improving the accur...
Michael S. Fox-Rabinovitz, Vladimir M. Krasnopolsk...
EUROCOLT
1997
Springer
15 years 1 months ago
Vapnik-Chervonenkis Dimension of Recurrent Neural Networks
Most of the work on the Vapnik-Chervonenkis dimension of neural networks has been focused on feedforward networks. However, recurrent networks are also widely used in learning app...
Pascal Koiran, Eduardo D. Sontag
IJON
2000
76views more  IJON 2000»
14 years 9 months ago
Fast neural network simulations with population density methods
The complexity of neural networks of the brain makes studying these networks through computer simulation challenging. Conventional methods, where one models thousands of individua...
Duane Q. Nykamp, Daniel Tranchina