Evolving simple feed-forward and recurrent ANNs for signal classification: A comparison
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Rivero:2009:IJCNN,
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author = "Daniel Rivero and Julian Dorado and Juan Rabufial and
Alejandro Pazos",
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title = "Evolving simple feed-forward and recurrent ANNs for
signal classification: A comparison",
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booktitle = "International Joint Conference on Neural Networks,
IJCNN 2009",
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year = "2009",
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pages = "2685--2692",
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address = "Atlanta, Georgia, USA",
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month = jun # " 14-19",
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keywords = "genetic algorithms, genetic programming, evolutionary
computation, feedforward neural nets, learning
(artificial intelligence), recurrent neural nets,
signal classification, EEG signals, classification
tasks, epileptic seizures, evolutionary method, machine
learning, parameter configurations, recurrent ANN,
signal classification, simple feedforward ANN",
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DOI = "doi:10.1109/IJCNN.2009.5178621",
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abstract = "Among all of the Machine Learning techniques used for
classification tasks, Artificial Neural Networks (ANNs)
have obtained much success in their applications.
However, their development usually requires a manual
effort from the human expert in which several parameter
configurations (architectures, training parameters,
etc) are tried. This paper proposes a new evolutionary
method that evolves ANNs without any participation from
the human expert. This system can be used to evolve
feed-forward and recurrent ANNs. A real-world problem
has been used to test the behaviour of this system:
detection of epileptic seizures in EEG signals. A
comparison of the results obtained using recurrent and
feedforward ANNs to solve this problem is presented in
this paper. This comparison shows the good accuracies
obtained by this method (almost 100percent). Moreover,
these results show an important feature: the system
tries to evolve simple ANNs, with a low number of
neurons and connections (in many cases, the networks
have only 1 hidden neuron).",
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notes = "also known as \cite{5178621}",
- }
Genetic Programming entries for
Daniel Rivero Cebrian
Julian Dorado
Juan Rabufial
Alejandro Pazos Sierra
Citations