Emgu CV Library Documentation
CvSVMTrainAuto Method (model, trainData, responses, varIdx, sampleIdx, parameters, kFold, cGrid, gammaGrid, pGrid, nuGrid, coefGrid, degreeGrid)
NamespacesEmgu.CV.MLMlInvokeCvSVMTrainAuto(IntPtr, IntPtr, IntPtr, IntPtr, IntPtr, MCvSVMParams, Int32, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid)

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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.
Declaration Syntax
C#Visual BasicVisual C++
public static bool CvSVMTrainAuto(
	IntPtr model,
	IntPtr trainData,
	IntPtr responses,
	IntPtr varIdx,
	IntPtr sampleIdx,
	MCvSVMParams parameters,
	int kFold,
	MCvParamGrid cGrid,
	MCvParamGrid gammaGrid,
	MCvParamGrid pGrid,
	MCvParamGrid nuGrid,
	MCvParamGrid coefGrid,
	MCvParamGrid degreeGrid
)
Public Shared Function CvSVMTrainAuto ( _
	model As IntPtr, _
	trainData As IntPtr, _
	responses As IntPtr, _
	varIdx As IntPtr, _
	sampleIdx As IntPtr, _
	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
public:
static bool CvSVMTrainAuto(
	IntPtr model, 
	IntPtr trainData, 
	IntPtr responses, 
	IntPtr varIdx, 
	IntPtr sampleIdx, 
	MCvSVMParams parameters, 
	int kFold, 
	MCvParamGrid cGrid, 
	MCvParamGrid gammaGrid, 
	MCvParamGrid pGrid, 
	MCvParamGrid nuGrid, 
	MCvParamGrid coefGrid, 
	MCvParamGrid degreeGrid
)
Parameters
model (IntPtr)
The SVM model
trainData (IntPtr)
The training data.
responses (IntPtr)
The response for the training data.
varIdx (IntPtr)
Can be null if not needed. When specified, identifies variables (features) of interest. It is a Matrix>int< of nx1
sampleIdx (IntPtr)
Can be null if not needed. When specified, identifies samples of interest. It is a Matrix>int< of nx1
parameters (MCvSVMParams)
The parameters for SVM
kFold (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 (MCvParamGrid)
cGrid
gammaGrid (MCvParamGrid)
gammaGrid
pGrid (MCvParamGrid)
pGrid
nuGrid (MCvParamGrid)
nuGrid
coefGrid (MCvParamGrid)
coedGrid
degreeGrid (MCvParamGrid)
degreeGrid
Return Value

[Missing <returns> documentation for M:Emgu.CV.ML.MlInvoke.CvSVMTrainAuto(System.IntPtr,System.IntPtr,System.IntPtr,System.IntPtr,System.IntPtr,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)]

Assembly: Emgu.CV.ML (Module: Emgu.CV.ML) Version: 1.0.0.0 (1.0.0.0)