Support Vector Machine
| All Members | Constructors | Methods | Properties | Fields | |
| Icon | Member | Description |
|---|---|---|
| SVM()()() |
Create a support Vector Machine
| |
| _ptr |
A pointer to the unmanaged object
(Inherited from UnmanagedObject.) | |
| Clear()()() |
Clear the statistic model
(Inherited from StatModel.) | |
| Dispose()()() |
The dispose function that implements IDisposable interface
(Inherited from DisposableObject.) | |
| Dispose(Boolean) |
Release the all the memory associate with this object
(Inherited from DisposableObject.) | |
| DisposeObject()()() |
Release all the memory associated with the SVM
(Overrides DisposableObject.DisposeObject()()().) | |
| Equals(Object) | (Inherited from Object.) | |
| Finalize()()() |
Destructor
(Inherited from DisposableObject.) | |
| GetDefaultGrid(SVM_PARAM_TYPE) |
Get the default parameter grid for the specific SVM type
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| GetHashCode()()() | Serves as a hash function for a particular type. (Inherited from Object.) | |
| GetSupportVector(Int32) |
The method retrieves a given support vector
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| GetSupportVectorCount()()() |
The method retrieves the number of support vectors
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| GetType()()() | Gets the Type of the current instance. (Inherited from Object.) | |
| GetVarCount()()() |
The method retrieves the number of vars
| |
| Load(String) |
Load the statistic model from file
(Inherited from StatModel.) | |
| MemberwiseClone()()() | Creates a shallow copy of the current Object. (Inherited from Object.) | |
| Predict(Matrix<(Of <(Single>)>)) |
Predicts response for the input sample.
| |
| Ptr |
Pointer to the unmanaged object
(Inherited from UnmanagedObject.) | |
| Save(String) |
Save the statistic model to file
(Inherited from StatModel.) | |
| ToString()()() | (Inherited from Object.) | |
| Train(Matrix<(Of <(Single>)>), Matrix<(Of <(Single>)>), Matrix<(Of <(Int32>)>), Matrix<(Of <(Int32>)>), SVMParams) |
Train the SVM model with the specific paramters
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| TrainAuto(Matrix<(Of <(Single>)>), Matrix<(Of <(Single>)>), Matrix<(Of <(Int32>)>), Matrix<(Of <(Int32>)>), 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.
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| TrainAuto(Matrix<(Of <(Single>)>), Matrix<(Of <(Single>)>), Matrix<(Of <(Int32>)>), Matrix<(Of <(Int32>)>), 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.
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