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The SVM type exposes the following members.
Constructors
| Name | Description | |
|---|---|---|
 | SVM |
Create a support Vector Machine
|
Methods
| Name | Description | |
|---|---|---|
| Clear |
Clear the statistic model
(Inherited from StatModel.) |
| Dispose |
The dispose function that implements IDisposable interface
(Inherited from DisposableObject.) |
 | DisposeObject |
Release all the memory associated with the SVM
(Overrides DisposableObject..::..DisposeObject()()()().) |
| Equals | (Inherited from Object.) |
| Finalize |
Destructor
(Inherited from DisposableObject.) |
 | GetDefaultGrid |
Get the default parameter grid for the specific SVM type
|
| GetHashCode |
Serves as a hash function for a particular type.
(Inherited from Object.) |
| GetSupportVector |
The method retrieves a given support vector
|
| GetSupportVectorCount |
The method retrieves the number of support vectors
|
| GetType |
Gets the Type of the current instance.
(Inherited from Object.) |
| GetVarCount |
The method retrieves the number of vars
|
| Load |
Load the statistic model from file
(Inherited from StatModel.) |
| MemberwiseClone |
Creates a shallow copy of the current Object.
(Inherited from Object.) |
| Predict |
Predicts response for the input sample.
|
| 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.) |
| Save |
Save the statistic model to file
(Inherited from StatModel.) |
| ToString | (Inherited from Object.) |
| Train |
Train the SVM model with the specific paramters
|
| TrainAuto(Matrix<(Of <<'(Single>)>>), Matrix<(Of <<'(Single>)>>), Matrix<(Of <<'(Byte>)>>), Matrix<(Of <<'(Byte>)>>), MCvSVMParams, 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(Matrix<(Of <<'(Single>)>>), Matrix<(Of <<'(Single>)>>), Matrix<(Of <<'(Byte>)>>), Matrix<(Of <<'(Byte>)>>), MCvSVMParams, Int32, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid) |
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.) |
Properties
| Name | Description | |
|---|---|---|
 | Parameters |
Get a copy of the SVM parameters
|
| Ptr |
Pointer to the unmanaged object
(Inherited from UnmanagedObject.) |
See Also
