Name
read_samples_class_svmread_samples_class_svmReadSamplesClassSvmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvm — Read the training data of a support vector machine from a file.
read_samples_class_svmread_samples_class_svmReadSamplesClassSvmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvm reads training samples from the file
given by FileNameFileNameFileNameFileNameFileNamefileName and adds them to the training samples
that have already been added to the support vector machine (SVM)
given by SVMHandleSVMHandleSVMHandleSVMHandleSVMHandleSVMHandle. The SVM must be created with
create_class_svmcreate_class_svmCreateClassSvmcreate_class_svmCreateClassSvmCreateClassSvm before calling
read_samples_class_svmread_samples_class_svmReadSamplesClassSvmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvm. As described with
train_class_svmtrain_class_svmTrainClassSvmtrain_class_svmTrainClassSvmTrainClassSvm and write_samples_class_svmwrite_samples_class_svmWriteSamplesClassSvmwrite_samples_class_svmWriteSamplesClassSvmWriteSamplesClassSvm, the
operators read_samples_class_svmread_samples_class_svmReadSamplesClassSvmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvm,
add_sample_class_svmadd_sample_class_svmAddSampleClassSvmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm, and write_samples_class_svmwrite_samples_class_svmWriteSamplesClassSvmwrite_samples_class_svmWriteSamplesClassSvmWriteSamplesClassSvm
can be used to build up a extensive set of training samples, and
hence to improve the performance of the SVM by retraining the SVM
with extended data sets.
It should be noted that the training samples must have the correct
dimensionality. The feature vectors and target vectors stored in
FileNameFileNameFileNameFileNameFileNamefileName must have the lengths NumFeatures and
NumClasses that were specified with
create_class_svmcreate_class_svmCreateClassSvmcreate_class_svmCreateClassSvmCreateClassSvm. The target is stored in vector form for
compatibility reason with the MLP (see
read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlp). If the dimensions are incorrect an
error message is returned.
- 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.
If the parameters are valid the operator
read_samples_class_svmread_samples_class_svmReadSamplesClassSvmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvm 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
add_sample_class_svmadd_sample_class_svmAddSampleClassSvmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm
write_samples_class_svmwrite_samples_class_svmWriteSamplesClassSvmwrite_samples_class_svmWriteSamplesClassSvmWriteSamplesClassSvm,
clear_samples_class_svmclear_samples_class_svmClearSamplesClassSvmclear_samples_class_svmClearSamplesClassSvmClearSamplesClassSvm
Foundation