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| Uses of LearningRate in org.encog.neural.networks.training.competitive |
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| Classes in org.encog.neural.networks.training.competitive that implement LearningRate | |
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CompetitiveTraining
This class implements competitive training, which would be used in a winner-take-all neural network, such as the self organizing map (SOM). |
| Uses of LearningRate in org.encog.neural.networks.training.cpn |
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| Classes in org.encog.neural.networks.training.cpn that implement LearningRate | |
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TrainInstar
Used for Instar training of a CPN neural network. |
class |
TrainOutstar
Used for Instar training of a CPN neural network. |
| Uses of LearningRate in org.encog.neural.networks.training.hebbian |
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| Classes in org.encog.neural.networks.training.hebbian that implement LearningRate | |
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class |
HebbianTraining
This class implements Hebbian learning for Enocg. |
| Uses of LearningRate in org.encog.neural.networks.training.propagation.back |
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| Classes in org.encog.neural.networks.training.propagation.back that implement LearningRate | |
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class |
Backpropagation
This class implements a backpropagation training algorithm for feed forward neural networks. |
| Uses of LearningRate in org.encog.neural.networks.training.propagation.manhattan |
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| Classes in org.encog.neural.networks.training.propagation.manhattan that implement LearningRate | |
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class |
ManhattanPropagation
One problem that the backpropagation technique has is that the magnitude of the partial derivative may be calculated too large or too small. |
| Uses of LearningRate in org.encog.neural.networks.training.simple |
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| Classes in org.encog.neural.networks.training.simple that implement LearningRate | |
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TrainAdaline
Train an ADALINE neural network. |
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