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» Information complexity of neural networks
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NN
2000
Springer
150views Neural Networks» more  NN 2000»
15 years 1 months ago
Multi-step-ahead prediction using dynamic recurrent neural networks
A method for the development of empirical predictive models for complex processes is presented. The models are capable of performing accurate multi-step-ahead (MS) predictions, wh...
Alexander G. Parlos, Omar T. Rais, Amir F. Atiya
ICANN
2005
Springer
15 years 7 months ago
Learning Features of Intermediate Complexity for the Recognition of Biological Motion
Humans can recognize biological motion from strongly impoverished stimuli, like point-light displays. Although the neural mechanism underlying this robust perceptual process have n...
Rodrigo Sigala, Thomas Serre, Tomaso Poggio, Marti...
TNN
1998
92views more  TNN 1998»
15 years 1 months ago
Inductive inference from noisy examples using the hybrid finite state filter
—Recurrent neural networks processing symbolic strings can be regarded as adaptive neural parsers. Given a set of positive and negative examples, picked up from a given language,...
Marco Gori, Marco Maggini, Enrico Martinelli, Giov...
GLOBECOM
2010
IEEE
14 years 11 months ago
Information Epidemics in Complex Networks with Opportunistic Links and Dynamic Topology
Abstract--Wireless networks, especially mobile ad hoc networks (MANET) and cognitive radio networks (CRN), are facing two new challenges beyond traditional random network model: op...
Pin-Yu Chen, Kwang-Cheng Chen
EVOW
2009
Springer
15 years 5 months ago
Efficient Signal Processing and Anomaly Detection in Wireless Sensor Networks
In this paper the node-level decision unit of a self-learning anomaly detection mechanism for office monitoring with wireless sensor nodes is presented. The node-level decision uni...
Markus Wälchli, Torsten Braun