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» Solving the Ill-Conditioning in Neural Network Learning
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WCE
2007
14 years 10 months ago
Neural Networks for Optimal Control of Aircraft Landing Systems
Abstract—In this work we present a variational formulation for a multilayer perceptron neural network. With this formulation any learning task for the neural network is defined ...
Kevin Lau, Roberto Lopez, Eugenio Oñate
70
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CONNECTION
2004
98views more  CONNECTION 2004»
14 years 9 months ago
Self-refreshing memory in artificial neural networks: learning temporal sequences without catastrophic forgetting
While humans forget gradually, highly distributed connectionist networks forget catastrophically: newly learned information often completely erases previously learned information. ...
Bernard Ans, Stephane Rousset, Robert M. French, S...
GECCO
2000
Springer
120views Optimization» more  GECCO 2000»
15 years 1 months ago
A Note on Learning and Evolution in Neural Networks
Interactions between evolution and lifetime learning are of great interest to studies of adaptive behaviour both in the natural world and the field of evolutionary computation. Th...
Brian Carse, Johan Oreland
ICML
2006
IEEE
15 years 10 months ago
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is...
Alex Graves, Faustino J. Gomez, Jürgen Schmid...
TNN
2010
234views Management» more  TNN 2010»
14 years 4 months ago
Novel maximum-margin training algorithms for supervised neural networks
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...
Oswaldo Ludwig, Urbano Nunes