SVM Methods

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