Modifier and Type | Class and Description |
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class |
TrainBayesian
Train a Bayesian network.
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Modifier and Type | Class and Description |
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class |
TrainGaussian |
Modifier and Type | Class and Description |
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class |
TrainLinearRegression |
Modifier and Type | Class and Description |
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class |
MLMethodGeneticAlgorithm
Implements a genetic algorithm that allows an MLMethod that is encodable
(MLEncodable) to be trained.
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Modifier and Type | Class and Description |
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class |
SVMSearchTrain
Provides training for Support Vector Machine networks.
|
class |
SVMTrain
Provides training for Support Vector Machine networks.
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Modifier and Type | Class and Description |
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class |
TrainInstar
Used for Instar training of a CPN neural network.
|
class |
TrainOutstar
Used for Instar training of a CPN neural network.
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Modifier and Type | Class and Description |
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class |
FreeformBackPropagation
Perform backpropagation for a freeform neural network.
|
class |
FreeformPropagationTraining
Provides basic propagation functions to other trainers.
|
class |
FreeformResilientPropagation |
Modifier and Type | Class and Description |
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class |
NeuralSimulatedAnnealing
This class implements a simulated annealing training algorithm for neural
networks.
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Modifier and Type | Class and Description |
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class |
CrossTraining
Base class for cross training trainers.
|
class |
CrossValidationKFold
Train using K-Fold cross validation.
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Modifier and Type | Class and Description |
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class |
LevenbergMarquardtTraining
Trains a neural network using a Levenberg Marquardt algorithm (LMA).
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Modifier and Type | Class and Description |
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class |
NelderMeadTraining
The Nelder-Mead method is a commonly used parameter optimization method that
can be used for neural network training.
|
Modifier and Type | Class and Description |
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class |
TrainBasicPNN
Train a PNN.
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Modifier and Type | Class and Description |
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class |
Propagation
Implements basic functionality that is needed by each of the propagation
methods.
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Modifier and Type | Class and Description |
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class |
Backpropagation
This class implements a backpropagation training algorithm for feed forward
neural networks.
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Modifier and Type | Class and Description |
---|---|
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.
|
Modifier and Type | Class and Description |
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class |
QuickPropagation
QPROP is an efficient training method that is based on Newton's Method.
|
Modifier and Type | Class and Description |
---|---|
class |
ResilientPropagation
One problem with the backpropagation algorithm is that the magnitude of the
partial derivative is usually too large or too small.
|
Modifier and Type | Class and Description |
---|---|
class |
ScaledConjugateGradient
This is a training class that makes use of scaled conjugate gradient methods.
|
Modifier and Type | Class and Description |
---|---|
class |
NeuralPSO
Iteratively trains a population of neural networks by applying
particle swarm optimisation (PSO).
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Modifier and Type | Class and Description |
---|---|
class |
TrainAdaline
Train an ADALINE neural network.
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Modifier and Type | Class and Description |
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class |
SVDTraining
Train a RBF neural network using a SVD.
|
Modifier and Type | Class and Description |
---|---|
class |
BasicTrainSOM
This class implements competitive training, which would be used in a
winner-take-all neural network, such as the self organizing map (SOM).
|
Modifier and Type | Class and Description |
---|---|
class |
SOMClusterCopyTraining
SOM cluster copy is a very simple trainer for SOM's.
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