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.
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.
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.
This operator modifies the state of the following input parameter:
The value of this parameter may not be shared across multiple threads without external synchronization.
Feature vector of the training sample to be stored.
Class of the training sample to be stored.
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.
create_class_svmcreate_class_svmCreateClassSvmcreate_class_svmCreateClassSvmCreateClassSvm
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
read_samples_class_svmread_samples_class_svmReadSamplesClassSvmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvm
clear_samples_class_svmclear_samples_class_svmClearSamplesClassSvmclear_samples_class_svmClearSamplesClassSvmClearSamplesClassSvm,
get_support_vector_class_svmget_support_vector_class_svmGetSupportVectorClassSvmget_support_vector_class_svmGetSupportVectorClassSvmGetSupportVectorClassSvm
Foundation