| SVM Class |
Support Vector Machine
Inheritance HierarchyNamespace: Emgu.CV.ML
Assembly: Emgu.CV.World (in Emgu.CV.World.dll) Version: 3.3.0.2824 (3.3.0.2824)
SyntaxThe SVM type exposes the following members.
ConstructorsProperties| Name | Description | |
|---|---|---|
 | C |
Parameter C of a SVM optimization problem
|
| Coef0 |
Parameter coef0 of a kernel function
|
| Degree |
Parameter degree of a kernel function
|
| Gamma |
Parameter gamma of a kernel function
|
| KernelType |
Type of a SVM kernel
|
| Nu |
Parameter nu of a SVM optimization problem
|
| P |
Parameter epsilon of a SVM optimization problem
|
| Ptr |
Pointer to the unmanaged object
(Inherited from UnmanagedObject.) |
| TermCriteria |
Termination criteria of the iterative SVM training procedure which solves a partial case of constrained quadratic optimization problem
|
| Type |
Type of a SVM formulation
|
Methods| Name | Description | |
|---|---|---|
 | Dispose |
The dispose function that implements IDisposable interface
(Inherited from DisposableObject.) |
 | DisposeObject |
Release all the memory associated with the SVM
(Overrides DisposableObjectDisposeObject.) |
| Equals | (Inherited from Object.) |
| Finalize |
Destructor
(Inherited from DisposableObject.) |
 | GetDefaultGrid |
Get the default parameter grid for the specific SVM type
|
| GetHashCode | (Inherited from Object.) |
| GetSupportVectors |
Retrieves all the support vectors.
|
| GetType | (Inherited from Object.) |
| MemberwiseClone | (Inherited from Object.) |
| ReleaseManagedResources |
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.) |
| SetKernel |
Initialize with one of predefined kernels
|
| ToString | (Inherited from Object.) |
| TrainAuto(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.
|
| TrainAuto(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.
|
Fields| Name | Description | |
|---|---|---|
 | _ptr |
A pointer to the unmanaged object
(Inherited from UnmanagedObject.) |
Extension Methods| Name | Description | |
|---|---|---|
 | Clear |
Clear the statistic model
(Defined by StatModelExtensions.) |
| Predict |
Predicts response(s) for the provided sample(s)
(Defined by StatModelExtensions.) |
| Read |
Reads algorithm parameters from a file storage.
(Defined by AlgorithmExtensions.) |
| Save |
Save the statistic model to file
(Defined by StatModelExtensions.) |
| Train(TrainData, Int32) | Overloaded.
Trains the statistical model.
(Defined by StatModelExtensions.) |
| Train(IInputArray, DataLayoutType, IInputArray) | Overloaded.
Trains the statistical model.
(Defined by StatModelExtensions.) |
| Write |
Stores algorithm parameters in a file storage
(Defined by AlgorithmExtensions.) |
See Also