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SVM Class
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Support Vector Machine
Inheritance Hierarchy

Namespace: Emgu.CV.ML
Assembly: Emgu.CV.ML (in Emgu.CV.ML.dll) Version: 3.0.0.2161 (3.0.0.2161)
Syntax
public class SVM : UnmanagedObject, IStatModel, IAlgorithm

The SVM type exposes the following members.

Constructors
  NameDescription
Public methodSVM
Create a support Vector Machine
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Methods
  NameDescription
Public methodDispose
The dispose function that implements IDisposable interface
(Inherited from DisposableObject.)
Protected methodDisposeObject
Release all the memory associated with the SVM
(Overrides DisposableObjectDisposeObject.)
Public methodEquals
Determines whether the specified Object is equal to the current Object.
(Inherited from Object.)
Protected methodFinalize
Destructor
(Inherited from DisposableObject.)
Public methodStatic memberGetDefaultGrid
Get the default parameter grid for the specific SVM type
Public methodGetHashCode
Serves as a hash function for a particular type.
(Inherited from Object.)
Public methodGetSupportVectors
Retrieves all the support vectors.
Public methodGetType
Gets the type of the current instance.
(Inherited from Object.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Protected methodReleaseManagedResources
Release the managed resources. This function will be called during the disposal of the current object. override ride this function if you need to call the Dispose() function on any managed IDisposable object created by the current object
(Inherited from DisposableObject.)
Public methodSetKernel
Initialize with one of predefined kernels
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
Public methodTrainAuto(TrainData, Int32)
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.
Public methodTrainAuto(TrainData, Int32, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid, Boolean)
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.
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Extension Methods
  NameDescription
Public Extension MethodClear
Clear the statistic model
(Defined by StatModelExtensions.)
Public Extension MethodPredict
Predicts response(s) for the provided sample(s)
(Defined by StatModelExtensions.)
Public Extension MethodRead
Reads algorithm parameters from a file storage.
(Defined by AlgorithmExtensions.)
Public Extension MethodSave
Save the statistic model to file
(Defined by StatModelExtensions.)
Public Extension MethodTrain(TrainData, Int32)Overloaded.
Trains the statistical model.
(Defined by StatModelExtensions.)
Public Extension MethodTrain(IInputArray, DataLayoutType, IInputArray)Overloaded.
Trains the statistical model.
(Defined by StatModelExtensions.)
Public Extension MethodWrite
Stores algorithm parameters in a file storage
(Defined by AlgorithmExtensions.)
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Fields
  NameDescription
Protected field_ptr
A pointer to the unmanaged object
(Inherited from UnmanagedObject.)
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Properties
  NameDescription
Public propertyC
Parameter C of a SVM optimization problem
Public propertyCoef0
Parameter coef0 of a kernel function
Public propertyDegree
Parameter degree of a kernel function
Public propertyGamma
Parameter gamma of a kernel function
Public propertyKernelType
Type of a SVM kernel
Public propertyNu
Parameter nu of a SVM optimization problem
Public propertyP
Parameter epsilon of a SVM optimization problem
Public propertyPtr
Pointer to the unmanaged object
(Inherited from UnmanagedObject.)
Public propertyTermCriteria
Termination criteria of the iterative SVM training procedure which solves a partial case of constrained quadratic optimization problem
Public propertyType
Type of a SVM formulation
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See Also