add_sample_class_svmT_add_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm (Operator)

Name

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

Signature

Description

add_sample_class_svmadd_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm adds a training sample to the support vector machine (SVM) given by SVMHandleSVMHandleSVMHandleSVMHandleSVMHandle. The training sample is given by FeaturesFeaturesFeaturesFeaturesfeatures and ClassClassClassClassclassVal. FeaturesFeaturesFeaturesFeaturesfeatures is the feature vector of the sample, and consequently must be a real vector of length NumFeatures, as specified in create_class_svmcreate_class_svmcreate_class_svmCreateClassSvmCreateClassSvm. ClassClassClassClassclassVal is the target of the sample, which must be in the range of 0 to NumClasses-1 (see create_class_svmcreate_class_svmcreate_class_svmCreateClassSvmCreateClassSvm). Note that ClassClassClassClassclassVal is ignored in the training if the classifier is used for novelty-detection. Before the SVM can be trained with train_class_svmtrain_class_svmtrain_class_svmTrainClassSvmTrainClassSvm, training samples must be added to the SVM with add_sample_class_svmadd_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm. The usage of support vectors of an already trained SVM as training samples is described in train_class_svmtrain_class_svmtrain_class_svmTrainClassSvmTrainClassSvm.

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

Normally, it is useful to save the training samples in a file with write_samples_class_svmwrite_samples_class_svmwrite_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_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.

Possible Predecessors

create_class_svmcreate_class_svmcreate_class_svmCreateClassSvmCreateClassSvm

Possible Successors

train_class_svmtrain_class_svmtrain_class_svmTrainClassSvmTrainClassSvm, write_samples_class_svmwrite_samples_class_svmwrite_samples_class_svmWriteSamplesClassSvmWriteSamplesClassSvm, get_sample_num_class_svmget_sample_num_class_svmget_sample_num_class_svmGetSampleNumClassSvmGetSampleNumClassSvm, get_sample_class_svmget_sample_class_svmget_sample_class_svmGetSampleClassSvmGetSampleClassSvm

Alternatives

read_samples_class_svmread_samples_class_svmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvm

See also

clear_samples_class_svmclear_samples_class_svmclear_samples_class_svmClearSamplesClassSvmClearSamplesClassSvm, get_support_vector_class_svmget_support_vector_class_svmget_support_vector_class_svmGetSupportVectorClassSvmGetSupportVectorClassSvm

Module

Foundation