add_sample_class_svm — Add a training sample to the training data of a support vector
add_sample_class_svm adds a training sample to the support
vector machine (SVM) given by
SVMHandle. The training
sample is given by
Features is the feature vector of the sample, and
consequently must be a real vector of length
as specified in
Class is the
target of the sample, which must be in the range of 0 to
create_class_svm). In the special
case of novelty detection the class is to be set to 0 as only one
class is assumed.
Before the SVM can be trained with
train_class_svm, training samples must be added to the
add_sample_class_svm. The usage of support vectors
of an already trained SVM as training samples is described in
The number of currently stored training samples can be queried with
get_sample_num_class_svm. Stored training samples can be
read out again with
Normally, it is useful to save the training samples in a file with
write_samples_class_svm to facilitate reusing the samples
and to facilitate that, if necessary, new training samples can be
added to the data set, and hence to facilitate that a newly
created SVM can be trained with the extended data set.
This operator modifies the state of the following input parameter:
During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.
SVMHandle(input_control, state is modified) class_svm
Feature vector of the training sample to be stored.
→(integer / real)
Class of the training sample to be stored.
If the parameters are valid the operator
add_sample_class_svm returns the value TRUE. If necessary,
an exception is raised.