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» On Generalization by Neural Networks
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119
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NCA
2007
IEEE
15 years 3 months ago
Using evolution to improve neural network learning: pitfalls and solutions
: Autonomous neural network systems typically require fast learning and good generalization performance, and there is potentially a trade-off between the two. The use of evolutiona...
John A. Bullinaria
138
Voted
GECCO
2006
Springer
178views Optimization» more  GECCO 2006»
15 years 7 months ago
A dynamic approach to artificial immune systems utilizing neural networks
The purpose of this work is to propose an immune-inspired setup to use a self-organizing map as a computational model for the interaction of antigens and antibodies. The proposed ...
Stefan Schadwinkel, Werner Dilger
111
Voted
CSB
2005
IEEE
166views Bioinformatics» more  CSB 2005»
15 years 9 months ago
Artificial Neural Networks to Predict Daylily Hybrids
Artificial Neural Networks (ANN) were employed to predict daylily (Hemerocalli spp.) hybrids from known characteristics of parents used in hybridization. Features such as height, ...
Ramana M. Gosukonda, Masoud Naghedolfeizi, Johnny ...
143
Voted
IJON
1998
119views more  IJON 1998»
15 years 3 months ago
Comment on "Recurrent neural networks: A constructive algorithm, and its properties"
In their paper [1], Tsoi and Tan present what they call a "canonical form", which they claim to be identical to that proposed in Nerrand et al [2]. They also claim that ...
Léon Personnaz, Gérard Dreyfus
CONNECTION
2004
98views more  CONNECTION 2004»
15 years 3 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...