train_class_knn — Creates the search trees for a k-NN classifier.
train_class_knn creates the search trees for
a k-NN classifier.
It is possible to set the number of trees via
'num_trees'. The default value for the number of search trees
is 4. A higher number of trees improves the accuracy of
the search, but increases the run time.
It is possible to add more samples after training using the operator
add_sample_class_knn. The added data affects the classification
train_class_knn is called again.
Automatic feature normalization can be activated by setting
GenParamName and 'true'
GenParamValue. The feature vectors are normalized by
normalizing each dimension separately. For each dimension, the mean and
standard deviation is calculated over the training samples. Every feature
vector is normalized by subtracting the mean and dividing by the standard
deviation of the individual dimension.
This results in a normalization, where each dimension has zero mean and
unit variance. If the standard deviation happens to be zero, only the mean
is subtracted. Please note however, that a feature dimension with no
standard deviation does not change the classification result and should be
removed. Automatic feature normalization will change the stored training
data, but the original data can be restored at any time by calling
train_class_knn with 'normalization' set to
'false'. If normalization is used, the operator
classify_class_knn interprets the input data as unnormalized and
performs normalization internally as it has been defined in the last call
This operator modifies the state of the following input parameter:
During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.
KNNHandle(input_control, state is modified) class_knn
Handle of the k-NN classifier.
Names of the generic parameters that can be adjusted for the k-NN classifier creation.
Default value: 
List of values: 'normalization', 'num_trees'
→(integer / string / real)
Values of the generic parameters that can be adjusted for the k-NN classifier creation.
Default value: 
Suggested values: 4, 'false', 'true'
If the parameters are valid, the operator
returns the value TRUE. If necessary, an exception is raised.
Marius Muja, David G. Lowe: “Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration”; International Conference on Computer Vision Theory and Applications (VISAPP 09); 2009.