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.ML (in Emgu.CV.ML.dll) Version: 2.4.10.1935 (2.4.10.1935)

Syntax

C#
public bool TrainAuto(
	Matrix<float> trainData,
	Matrix<float> responses,
	Matrix<byte> varIdx,
	Matrix<byte> sampleIdx,
	MCvSVMParams parameters,
	int kFold,
	MCvParamGrid cGrid,
	MCvParamGrid gammaGrid,
	MCvParamGrid pGrid,
	MCvParamGrid nuGrid,
	MCvParamGrid coefGrid,
	MCvParamGrid degreeGrid
)
Visual Basic
Public Function TrainAuto ( 
	trainData As Matrix(Of Single),
	responses As Matrix(Of Single),
	varIdx As Matrix(Of Byte),
	sampleIdx As Matrix(Of Byte),
	parameters As MCvSVMParams,
	kFold As Integer,
	cGrid As MCvParamGrid,
	gammaGrid As MCvParamGrid,
	pGrid As MCvParamGrid,
	nuGrid As MCvParamGrid,
	coefGrid As MCvParamGrid,
	degreeGrid As MCvParamGrid
) As Boolean
Visual C++
public:
bool TrainAuto(
	Matrix<float>^ trainData, 
	Matrix<float>^ responses, 
	Matrix<unsigned char>^ varIdx, 
	Matrix<unsigned char>^ sampleIdx, 
	MCvSVMParams parameters, 
	int kFold, 
	MCvParamGrid cGrid, 
	MCvParamGrid gammaGrid, 
	MCvParamGrid pGrid, 
	MCvParamGrid nuGrid, 
	MCvParamGrid coefGrid, 
	MCvParamGrid degreeGrid
)
F#
member TrainAuto : 
        trainData : Matrix<float32> * 
        responses : Matrix<float32> * 
        varIdx : Matrix<byte> * 
        sampleIdx : Matrix<byte> * 
        parameters : MCvSVMParams * 
        kFold : int * 
        cGrid : MCvParamGrid * 
        gammaGrid : MCvParamGrid * 
        pGrid : MCvParamGrid * 
        nuGrid : MCvParamGrid * 
        coefGrid : MCvParamGrid * 
        degreeGrid : MCvParamGrid -> bool 

Parameters

trainData
Type: Emgu.CV..::..Matrix<(Of <(<'Single>)>)>
The training data.
responses
Type: Emgu.CV..::..Matrix<(Of <(<'Single>)>)>
The response for the training data.
varIdx
Type: Emgu.CV..::..Matrix<(Of <(<'Byte>)>)>
Can be null if not needed. When specified, identifies variables (features) of interest. It is a Matrix<int> of nx1
sampleIdx
Type: Emgu.CV..::..Matrix<(Of <(<'Byte>)>)>
Can be null if not needed. When specified, identifies samples of interest. It is a Matrix<int> of nx1
parameters
Type: Emgu.CV.ML.Structure..::..MCvSVMParams
The parameters for SVM
kFold
Type: System..::..Int32
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.ML.Structure..::..MCvParamGrid
cGrid
gammaGrid
Type: Emgu.CV.ML.Structure..::..MCvParamGrid
gammaGrid
pGrid
Type: Emgu.CV.ML.Structure..::..MCvParamGrid
pGrid
nuGrid
Type: Emgu.CV.ML.Structure..::..MCvParamGrid
nuGrid
coefGrid
Type: Emgu.CV.ML.Structure..::..MCvParamGrid
coedGrid
degreeGrid
Type: Emgu.CV.ML.Structure..::..MCvParamGrid
degreeGrid

Return Value

Type: Boolean

[Missing <returns> documentation for "M:Emgu.CV.ML.SVM.TrainAuto(Emgu.CV.Matrix{System.Single},Emgu.CV.Matrix{System.Single},Emgu.CV.Matrix{System.Byte},Emgu.CV.Matrix{System.Byte},Emgu.CV.ML.Structure.MCvSVMParams,System.Int32,Emgu.CV.ML.Structure.MCvParamGrid,Emgu.CV.ML.Structure.MCvParamGrid,Emgu.CV.ML.Structure.MCvParamGrid,Emgu.CV.ML.Structure.MCvParamGrid,Emgu.CV.ML.Structure.MCvParamGrid,Emgu.CV.ML.Structure.MCvParamGrid)"]

See Also