SVM Class

Creates instance by raw pointer cv::ml::SVM*

Gets or sets a memory address allocated by AllocMemory.

Gets or sets the byte length of the allocated memory

Parameter C of a %SVM optimization problem. For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0.

Optional weights in the SVM::C_SVC problem, assigned to particular classes.

Parameter coef0 of a kernel function. For SVM::POLY or SVM::SIGMOID. Default value is 0.

Native pointer of OpenCV structure

Gets or sets a handle which allocates using cvSetData.

Parameter degree of a kernel function. For SVM::POLY. Default value is 0.

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

Parameter gamma of a kernel function. For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1.

Gets a value indicating whether this instance has been disposed.

Gets or sets a value indicating whether you permit disposing this instance.

Type of a %SVM kernel. See SVM::KernelTypes. Default value is SVM::RBF.

Parameter nu of a %SVM optimization problem. For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0.

Parameter epsilon of a %SVM optimization problem. For SVM::EPS_SVR. Default value is 0.

Termination criteria of the iterative SVM training procedure which solves a partial case of constrained quadratic optimization problem.

Type of a %SVM formulation. Default value is SVM::C_SVC.

Pins the object to be allocated by cvSetData.

Allocates the specified size of memory.

Computes error on the training or test dataset

Creates empty model. Use StatModel::Train to train the model. Since %SVM has several parameters, you may want to find the best parameters for your problem, it can be done with SVM::TrainAuto.

Releases the resources

Releases the resources

Releases managed resources

releases unmanaged resources

(Inherited from StatModel.)

Determines whether the specified object is equal to the current object.

Destructor

Retrieves the decision function

Generates a grid for SVM parameters.

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Serves as the default hash function.

Retrieves all the support vectors

Gets the Type of the current instance.

Returns the number of variables in training samples

Returns true if the model is classifier

Returns true if the model is trained

Loads and creates a serialized svm from a file. Use SVM::save to serialize and store an SVM to disk. Load the SVM from this file again, by calling this function with the path to the file.

Loads algorithm from a String.

Creates a shallow copy of the current Object.

Notifies the allocated size of memory.

Predicts response(s) for the provided sample(s)

Reads algorithm parameters from a file storage

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage fs).

Initialize with custom kernel.

If this object is disposed, then ObjectDisposedException is thrown.

Returns a string that represents the current object.

Trains the statistical model

Trains the statistical model

Trains an %SVM with optimal parameters.

Stores algorithm parameters in a file storage

Data pointer