C# Class Encog.ML.SVM.Training.SVMTrain

Inheritance: Encog.ML.Train.BasicTraining

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Méthodes publiques

Méthode	Description
Iteration ( ) : void	Perform either a train or a cross validation. If the folds property is greater than 1 then cross validation will be done. Cross validation does not produce a usable model, but it does set the error. If you are cross validating try C and Gamma values until you have a good error rate. Then use those values to train, producing the final model.
Pause ( ) : TrainingContinuation
Resume ( TrainingContinuation state ) : void
SVMTrain ( SupportVectorMachine method, IMLDataSet dataSet ) : Encog.MathUtil.Error	Construct a trainer for an SVM network.

Private Methods

Méthode	Description
Evaluate ( svm_parameter param, svm_problem prob, double target ) : double	Evaluate the error for the specified model.

Method Details

Iteration() public final méthode

Perform either a train or a cross validation. If the folds property is greater than 1 then cross validation will be done. Cross validation does not produce a usable model, but it does set the error. If you are cross validating try C and Gamma values until you have a good error rate. Then use those values to train, producing the final model.

public final Iteration ( ) : void
Résultat	void

Pause() public final méthode

public final Pause ( ) : TrainingContinuation
Résultat	Encog.Neural.Networks.Training.Propagation.TrainingContinuation

Resume() public méthode

public Resume ( TrainingContinuation state ) : void
state	Encog.Neural.Networks.Training.Propagation.TrainingContinuation
Résultat	void

SVMTrain() public méthode

Construct a trainer for an SVM network.

public SVMTrain ( SupportVectorMachine method, IMLDataSet dataSet ) : Encog.MathUtil.Error
method	Encog.ML.SVM.SupportVectorMachine	The network to train.
dataSet	IMLDataSet	The training data for this network.
Résultat	Encog.MathUtil.Error