| Table of Contents / Classification / Support Vector Machines | Operators |
get_prep_info_class_svm — Compute the information content of the preprocessed feature vectors of a support vector machine
get_prep_info_class_svm( : : SVMHandle, Preprocessing : InformationCont, CumInformationCont)
get_prep_info_class_svm computes the information content of the training vectors that have been transformed with the preprocessing given by Preprocessing. Preprocessing can be set to 'principal_components' or 'canonical_variates'. The preprocessing methods are described with create_class_svm. The information content is derived from the variations of the transformed components of the feature vector, i.e., it is computed solely based on the training data, independent of any error rate on the training data. The information content is computed for all relevant components of the transformed feature vectors (NumFeatures for 'principal_components' and min(NumClasses - 1, NumFeatures) for 'canonical_variates', see create_class_svm), and is returned in InformationCont as a number between 0 and 1. To convert the information content into a percentage, it simply needs to be multiplied by 100. The cumulative information content of the first n components is returned in the n-th component of CumInformationCont, i.e., CumInformationCont contains the sums of the first n elements of InformationCont. To use get_prep_info_class_svm, a sufficient number of samples must be added to the support vector machine (SVM) given by SVMHandle by using add_sample_class_svm or read_samples_class_svm.
InformationCont and CumInformationCont can be used to decide how many components of the transformed feature vectors contain relevant information. An often used criterion is to require that the transformed data must represent x% (e.g., 90%) of the data. This can be decided easily from the first value of CumInformationCont that lies above x%. The number thus obtained can be used as the value for NumComponents in a new call to create_class_svm. The call to get_prep_info_class_svm already requires the creation of an SVM, and hence the setting of NumComponents in create_class_svm to an initial value. However, when get_prep_info_class_svm is called, it is typically not known how many components are relevant, and hence how to set NumComponents in this call. Therefore, the following two-step approach should typically be used to select NumComponents: In a first step, an SVM with the maximum number for NumComponents is created (NumFeatures for 'principal_components' and min(NumClasses - 1, NumFeatures) for 'canonical_variates'). Then, the training samples are added to the SVM and are saved in a file using write_samples_class_svm. Subsequently, get_prep_info_class_svm is used to determine the information content of the components, and with this NumComponents. After this, a new SVM with the desired number of components is created, and the training samples are read with read_samples_class_svm. Finally, the SVM is trained with train_class_svm.
SVM handle.
Type of preprocessing used to transform the feature vectors.
Default value: 'principal_components'
List of values: 'principal_components', 'canonical_variates'
Relative information content of the transformed feature vectors.
Cumulative information content of the transformed feature vectors.
* Create the initial SVM
create_class_svm (NumFeatures, 'rbf', 0.01, 0.01, NumClasses,\
'one-versus-all', 'normalization', NumFeatures,\
SVMHandle)
* Generate and add the training data
for J := 0 to NData-1 by 1
* Generate training features and classes
* Data = [...]
* Class = ...
add_sample_class_svm (SVMHandle, Data, Class)
endfor
write_samples_class_svm (SVMHandle, 'samples.mtf')
* Compute the information content of the transformed features
get_prep_info_class_svm (SVMHandle, 'principal_components',\
InformationCont, CumInformationCont)
* Determine NComp by inspecting InformationCont and CumInformationCont
* NComp = [...]
clear_class_svm (SVMHandle)
* Create the actual SVM
create_class_svm (NumFeatures, 'rbf', 0.01, 0.01, NumClasses,\
'one-versus-all', 'principal_components', NComp, SVMHandle)
* Train the SVM
read_samples_class_svm (SVMHandle, 'samples.mtf')
train_class_svm (SVMHandle, 0.001, 'default')
write_class_svm (SVMHandle, 'classifier.svm')
clear_class_svm (SVMHandle)
If the parameters are valid the operator get_prep_info_class_svm returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.
get_prep_info_class_svm may return the error 9211 (Matrix is not positive definite) if Preprocessing = 'canonical_variates' is used. This typically indicates that not enough training samples have been stored for each class.
add_sample_class_svm, read_samples_class_svm
clear_class_svm, create_class_svm
Christopher M. Bishop: “Neural Networks for Pattern Recognition”;
Oxford University Press, Oxford; 1995.
Andrew Webb: “Statistical Pattern Recognition”; Arnold, London;
1999.
Foundation
| Table of Contents / Classification / Support Vector Machines | Operators |
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