learn_sampset_box — Train the classifier with one data set.
learn_sampset_box is obsolete and is only provided for
reasons of backward compatibility. New applications should use the
MLP, SVM, KNN or GMM operators instead.
learn_sampset_box trains the classifier with data for the
read_sampset). The training sequence
is terminated at least after
NSamples is bigger than the number of examples in
SampKey, then a cyclic start at the beginning occurs.
If the error underpasses the value
StopError, then the training
sequence is prematurely terminated.
StopError is calculated with
N / ErrorN. Whereby N means the number of examples which were wrong
classified during the last
ErrorN training examples.
ErrorN is the number of examples in
NSamples is a multiple of it.
If you want a data set with 100 examples to run 5 times at most and if you
want it to terminate with an error lower than 5%, then the corresponding
NSamples = 500,
ErrorN = 100 and
StopError = 0.05.
A protocol of the training activity is going to be written in file
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.
ClassifHandle(input_control, state is modified) class_box
Handle of the classifier.
Number of the data set to train.
Name of the protocol file.
Default value: 'training_prot'
Number of arrays of attributes to learn.
Default value: 500
Classification error for termination.
Default value: 0.05
Error during the assignment.
Default value: 100
learn_sampset_box returns 2 (H_MSG_TRUE).
An exception is raised if key
SampKey does not exist
or there are problems while opening the file.