ECNN
Evolving Cascade Neural Network (ECNN) Matlab script
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A new learning algorithm for Evolving Cascade Neural Networks (ECNNs) is described. An ECNN starts to learn with one input node and then adding new inputs as well as new hidden neurons evolves it. The trained ECNN has a nearly minimal number of input and hidden neurons as well as connections. The algorithm was successfully applied to classify artifacts and normal segments in clinical electroencephalograms (EEGs). The EEG segments were visually labeled by EEG-viewer. The trained ECNN has correctly classified 96.69 segments. It is slightly better than a standard fully connected neural network.
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Evolving Cascade Neural Networks (ECNNs) and a new training algorithm ca...
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A new technique is presented developed to learn multi-class concepts fro...
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A neural network based technique is presented, which is able to successf...
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This paper is concerned with the sparsification of the input-hidden weig...
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To learn the multi-class conceptions from the electroencephalogram (EEG)...
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The neural networks have trained on incomplete sets that a doctor could
...
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We describe a polynomial network technique developed for learning to cla...
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Evolving Cascade Neural Network (ECNN) Matlab script
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