add_sample_class_svmT_add_sample_class_svmAddSampleClassSvmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm (Operator)

Name

add_sample_class_svmT_add_sample_class_svmAddSampleClassSvmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm — Add a training sample to the training data of a support vector machine.

Signature

Description

add_sample_class_svmadd_sample_class_svmAddSampleClassSvmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm adds a training sample to the support vector machine (SVM) given by SVMHandleSVMHandleSVMHandleSVMHandleSVMHandleSVMHandle. The training sample is given by FeaturesFeaturesFeaturesFeaturesFeaturesfeatures and ClassClassClassClassClassclassVal. FeaturesFeaturesFeaturesFeaturesFeaturesfeatures is the feature vector of the sample, and consequently must be a real vector of length NumFeatures, as specified in create_class_svmcreate_class_svmCreateClassSvmcreate_class_svmCreateClassSvmCreateClassSvm. ClassClassClassClassClassclassVal is the target of the sample, which must be in the range of 0 to NumClasses-1 (see create_class_svmcreate_class_svmCreateClassSvmcreate_class_svmCreateClassSvmCreateClassSvm). 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_svmtrain_class_svmTrainClassSvmtrain_class_svmTrainClassSvmTrainClassSvm, training samples must be added to the SVM with add_sample_class_svmadd_sample_class_svmAddSampleClassSvmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm. The usage of support vectors of an already trained SVM as training samples is described in train_class_svmtrain_class_svmTrainClassSvmtrain_class_svmTrainClassSvmTrainClassSvm.

The number of currently stored training samples can be queried with get_sample_num_class_svmget_sample_num_class_svmGetSampleNumClassSvmget_sample_num_class_svmGetSampleNumClassSvmGetSampleNumClassSvm. Stored training samples can be read out again with get_sample_class_svmget_sample_class_svmGetSampleClassSvmget_sample_class_svmGetSampleClassSvmGetSampleClassSvm.

Normally, it is useful to save the training samples in a file with write_samples_class_svmwrite_samples_class_svmWriteSamplesClassSvmwrite_samples_class_svmWriteSamplesClassSvmWriteSamplesClassSvm 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.

Parallelization

• Multithreading type: exclusive (runs in parallel only with independent operators).
• Multithreading scope: global (may be called from any thread).
• Processed without parallelization.

Parameters

Result

If the parameters are valid the operator add_sample_class_svmadd_sample_class_svmAddSampleClassSvmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.

Possible Predecessors

create_class_svmcreate_class_svmCreateClassSvmcreate_class_svmCreateClassSvmCreateClassSvm

Possible Successors

train_class_svmtrain_class_svmTrainClassSvmtrain_class_svmTrainClassSvmTrainClassSvm, write_samples_class_svmwrite_samples_class_svmWriteSamplesClassSvmwrite_samples_class_svmWriteSamplesClassSvmWriteSamplesClassSvm, get_sample_num_class_svmget_sample_num_class_svmGetSampleNumClassSvmget_sample_num_class_svmGetSampleNumClassSvmGetSampleNumClassSvm, get_sample_class_svmget_sample_class_svmGetSampleClassSvmget_sample_class_svmGetSampleClassSvmGetSampleClassSvm

Alternatives

read_samples_class_svmread_samples_class_svmReadSamplesClassSvmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvm

See also

clear_samples_class_svmclear_samples_class_svmClearSamplesClassSvmclear_samples_class_svmClearSamplesClassSvmClearSamplesClassSvm, get_support_vector_class_svmget_support_vector_class_svmGetSupportVectorClassSvmget_support_vector_class_svmGetSupportVectorClassSvmGetSupportVectorClassSvm

Module

Foundation