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learn_sampset_box is obsolete and is only provided for reasons of backward compatibility.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 key SampKey (see read_sampset). The training sequence is terminated at least after NSamples examples. If 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. Typically ErrorN is the number of examples in SampKey and 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 values are NSamples = 500, ErrorN = 100 and StopError = 0.05. A protocol of the training activity is going to be written in file Outfile.
This operator modifies the state of the following input parameter:
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.
test_sampset_box, enquire_class_box, write_class_box, close_class_box, clear_sampset
test_sampset_box, enquire_class_box, learn_class_box, read_sampset