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| Uses of Propagation in org.encog.neural.networks.training.propagation |
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| Constructors in org.encog.neural.networks.training.propagation with parameters of type Propagation | |
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GradientWorker(FlatNetwork theNetwork,
Propagation theOwner,
MLDataSet theTraining,
int theLow,
int theHigh,
double[] flatSpot,
ErrorFunction ef)
Construct a gradient worker. |
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| Uses of Propagation in org.encog.neural.networks.training.propagation.back |
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| Subclasses of Propagation in org.encog.neural.networks.training.propagation.back | |
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class |
Backpropagation
This class implements a backpropagation training algorithm for feed forward neural networks. |
| Uses of Propagation in org.encog.neural.networks.training.propagation.manhattan |
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| Subclasses of Propagation in org.encog.neural.networks.training.propagation.manhattan | |
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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 Propagation in org.encog.neural.networks.training.propagation.quick |
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| Subclasses of Propagation in org.encog.neural.networks.training.propagation.quick | |
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class |
QuickPropagation
QPROP is an efficient training method that is based on Newton's Method. |
| Uses of Propagation in org.encog.neural.networks.training.propagation.resilient |
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| Subclasses of Propagation in org.encog.neural.networks.training.propagation.resilient | |
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class |
ResilientPropagation
One problem with the backpropagation algorithm is that the magnitude of the partial derivative is usually too large or too small. |
| Uses of Propagation in org.encog.neural.networks.training.propagation.scg |
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| Subclasses of Propagation in org.encog.neural.networks.training.propagation.scg | |
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class |
ScaledConjugateGradient
This is a training class that makes use of scaled conjugate gradient methods. |
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