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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Andina, D. Marcano-Cedeno, A. Torres, J. Alarcon, M.J. |
| Copyright Year | 2009 |
| Description | Author affiliation: Group for Automation in Signals and Communications., Technical University of Madrid (UPM)., Madrid, Spain (Andina, D.; Marcano-Cedeno, A.; Torres, J.; Alarcon, M.J.) |
| Abstract | In this work we tested and compared Artificial Metaplasticity (AMP) results for Multilayer Perceptrons (MLPs). AMP is a novel Artificial Neural Network (ANN) training algorithm inspired on the biological metaplasticity property of neurons and Shannon's information theory. During training phase, AMP training algorithm gives more relevance to less frequent patterns and subtracts relevance to the frequent ones, claiming to achieve a much more efficient training, while at least maintaining the MLP performance. AMP is specially recommended when few patterns are available to train the network. We implement an Artificial Metaplasticity MLP (AMMLP) on standard and well-used databases for Machine Learning. Experimental results show the superiority of AMMLPs when compared with recent results on the same databases. |
| Starting Page | 4256 |
| Ending Page | 4261 |
| File Size | 153451 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424427932 |
| ISSN | 1062922X |
| DOI | 10.1109/ICSMC.2009.5346818 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-10-11 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Testing Neurons Artificial neural networks Databases Backpropagation algorithms Biological system modeling Biological information theory Aerospace industry Cybernetics Automation Metaplasticity Neural Network MLPs Backpropagation Algorithm Pattern Recognition |
| Content Type | Text |
| Resource Type | Article |
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