select_feature_set_trainf_svm — Selects an optimal combination of features to classify OCR data.
select_feature_set_trainf_svm selects an optimal combination of features, to classify the data given in the training file TrainingFile with a Support Vector Machine (SVM), for details see create_ocr_class_svm.
Possible features are all OCR features listed and explained in create_ocr_class_svm. All candidates which should be tested can be specified in FeatureList. A subset of these features is returned as selected features in FeatureSet.
select_feature_set_trainf_svm is specialized on OCR problems and only supports the features in the list mentioned before. In order to use other features, please use the more general operator select_feature_set_svm.
The selection method SelectionMethod is either a greedy search 'greedy' (iteratively add the feature with highest gain) or the dynamically oscillating search 'greedy_oscillating' (add the feature with highest gain and test then if any of the already added features can be left out without great loss). The method 'greedy' is generally preferable, since it is faster. Only in cases when a large training set is available the method 'greedy_oscillating' might return better results.
The optimization criterion is the classification rate of a two-fold cross-validation of the training data. The best achieved value is returned in Score.
The parameters 'nu' and 'gamma' for the SVM that is used to classify can be set to 'auto' by using the parameters GenParamNames and GenParamValues. If they are set to 'auto', the estimated optimal 'nu' and/or 'gamma' is estimated. The automatic estimation of 'nu' and 'gamma' can take a substantial amount of time (up to days, depending on the data set and the number of features). Alternatively, a certain value for both can be set the same way. An explanation of the parameters 'nu' and 'gamma' as the kernel parameter of the radial basis function (RBF) kernel can be found in create_class_svm.
This operator may take considerable time, depending on the size of the data set in the training file, and the number of features.
Please note, that this operator should not be called, if only a small set of training data is available. Due to the risk of overfitting the operator select_feature_set_trainf_svm may deliver a classifier with a very high score. However, the classifier may perfom poorly when tested.
This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.
Names of the training files.
Default value: ''
File extension: .trf, .otr
List of features that should be considered for selection.
Default value: ['zoom_factor','ratio','width','height','foreground','foreground_grid_9','foreground_grid_16','anisometry','compactness','convexity','moments_region_2nd_invar','moments_region_2nd_rel_invar','moments_region_3rd_invar','moments_central','phi','num_connect','num_holes','projection_horizontal','projection_vertical','projection_horizontal_invar','projection_vertical_invar','chord_histo','num_runs','pixel','pixel_invar','pixel_binary','gradient_8dir','cooc','moments_gray_plane']
List of values: 'anisometry', 'chord_histo', 'compactness', 'convexity', 'cooc', 'default', 'foreground', 'foreground_grid_16', 'foreground_grid_9', 'gradient_8dir', 'height', 'moments_central', 'moments_gray_plane', 'moments_region_2nd_invar', 'moments_region_2nd_rel_invar', 'moments_region_3rd_invar', 'num_connect', 'num_holes', 'num_runs', 'phi', 'pixel', 'pixel_binary', 'pixel_invar', 'projection_horizontal', 'projection_horizontal_invar', 'projection_vertical', 'projection_vertical_invar', 'ratio', 'width', 'zoom_factor'
Method to perform the selection.
Default value: 'greedy'
List of values: 'greedy', 'greedy_oscillating'
Width of the rectangle to which the gray values of the segmented character are zoomed.
Default value: 15
Height of the rectangle to which the gray values of the segmented character are zoomed.
Default value: 16
Names of generic parameters to configure the selection process and the classifier.
Default value: 
List of values: 'gamma', 'nu'
Values of generic parameters to configure the selection process and the classifier.
Default value: 
Suggested values: 'auto', '0.1', '0.3'
Trained OCR-SVM Classifier.
Selected feature set, contains only entries from FeatureList.
Achieved score using tow-fold cross-validation.
If the parameters are valid, the operator select_feature_set_trainf_svm returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.
select_feature_set_trainf_mlp, select_feature_set_trainf_knn, select_feature_set_trainf_mlp_protected