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: 2.4.2.1777 (2.4.2.1777)

Syntax

C#
public bool TrainAuto(
	Matrix<float> trainData,
	Matrix<float> responses,
	Matrix<byte> varIdx,
	Matrix<byte> sampleIdx,
	MCvSVMParams parameters,
	int kFold
)
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 _
) 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
)

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

Return Value

[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)"]

See Also