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SVMTrainAuto Method (TrainData, Int32, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid, Boolean)

http://www.emgu.com
The method trains the SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree from CvSVMParams. By the optimality one mean that the cross-validation estimate of the test set error is minimal.

Namespace:  Emgu.CV.ML
Assembly:  Emgu.CV.World (in Emgu.CV.World.dll) Version: 4.0.1.3373 (4.0.1.3373)
Syntax
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public bool TrainAuto(
	TrainData trainData,
	int kFold,
	MCvParamGrid cGrid,
	MCvParamGrid gammaGrid,
	MCvParamGrid pGrid,
	MCvParamGrid nuGrid,
	MCvParamGrid coefGrid,
	MCvParamGrid degreeGrid,
	bool balanced = false
)

Parameters

trainData
Type: Emgu.CV.MLTrainData
The training data.
kFold
Type: SystemInt32
Cross-validation parameter. The training set is divided into k_fold subsets, one subset being used to train the model, the others forming the test set. So, the SVM algorithm is executed k_fold times
cGrid
Type: Emgu.CV.MLMCvParamGrid
cGrid
gammaGrid
Type: Emgu.CV.MLMCvParamGrid
grid for gamma
pGrid
Type: Emgu.CV.MLMCvParamGrid
grid for p
nuGrid
Type: Emgu.CV.MLMCvParamGrid
grid for nu
coefGrid
Type: Emgu.CV.MLMCvParamGrid
grid for coeff
degreeGrid
Type: Emgu.CV.MLMCvParamGrid
grid for degree
balanced (Optional)
Type: SystemBoolean
If true and the problem is 2-class classification then the method creates more balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset.

Return Value

Type: Boolean
True if training is successful.
See Also